Abstract Prediction intervals (PIs) are a practical tool for uncertainty quantification in regression, but comparatively little work has addressed fully incremental PI generation for data streams. In streaming settings, data arrive continuously, each instance is typically processed once, and concept drift can quickly invalidate a previously well-calibrated interval. These properties make many batch PI methods and window-based adaptations difficult to apply efficiently. This paper studies Adaptive Prediction Interval (AdaPI), an online post-calibration framework that adjusts interval width according to observed coverage. We instantiate the framework with a fully incremental variant of Mean and Variance Estimation (MVE) and investigate three adaptive scaling functions. We also adopt an evaluation perspective that jointly considers coverage accuracy and interval width. Experiments on a collection of real-world and synthetic regression streams show that AdaPI can often move coverage closer to the desired confidence level while maintaining competitive interval width; under the default 95% confidence setting and coverage-heavy CING weighting, the linear variant frequently gives the strongest empirical coverage–width trade-off among the three adaptive strategies.

Semi-supervised learning (SSL) problems are challenging, appear in many domains, and are particularly relevant to streaming applications, where data are abundant but labels are not. The problem tackled here is classification over an evolving data stream where labels are rare and distributed randomly. We propose SLEADE (Stream LEArning by Disagreement Ensemble), a novel method that exploits disagreement-based learning and unsupervised drift detection to leverage unlabeled data during training. SLEADE uses pseudo-labeled instances to augment the training set of each member of an ensemble using amajority trains minorityscheme. The pseudo-labeled data impact is controlled by a weighting function that considers the confidence in the prediction attributed by the ensemble members. SLEADE exploits unsupervised drift detection, which allows the ensemble to respond to changes. We present several experiments using real and synthetic data to illustrate the benefits and limitations of SLEADE compared to existing algorithms.

Catastrophic forgetting is when a neural network loses previously learnt information after learning a new task sequentially. Avoiding catastrophic forgetting could reduce the resources necessary to update neural networks. Recently, Kolmogorov–Arnold Networks (KAN) gained the community's attention as preliminary experiments suggest KAN avoid catastrophic forgetting. KAN replace neural network edges with learnable B-splines and sum incoming edges in nodes. Proponents of KAN argue they avoid forgetting, are more accurate, are interpretable, and use fewer parameters. Our work investigates the claims that KAN avoid catastrophic forgetting, finding that they fail to do so on more complex datasets containing features that overlap between tasks. We give a simple explanation as to why and how KAN catastrophically forget. Motivated by evidence suggesting KAN are superior for symbolic regression, we augment KAN in the same ways as multilayer perceptron (MLP) to perform continual learning tasks, making special accommodations to support KAN. Our experiments found that unmodified KAN often forget more than MLP, but KAN can be better than MLP when combined with continual learning strategies. We aim to highlight some of the current shortcomings and strengths associated with KAN for continual learning.

Ensemble methods are widely recognized for their effectiveness in data stream classification. This paper introduces Dynamic Ensemble Member Selection (DEMS), a novel framework that dynamically selects a subset of classifiers from an ensemble for each individual prediction. DEMS ranks base learners based on estimated accuracy and predictive margin, using only the top-K members for prediction, where K is optimized in a self-adaptive manner. The proposed method significantly enhances predictive performance across various state-of-the-art ensemble algorithms, including Streaming Random Patches, Adaptive Random Forest, and Online Smooth Boost. Experimental results demonstrate that DEMS consistently improves classification accuracy while maintaining a minimal runtime overhead of just 11.66% compared to the original methods. This work highlights the potential of DEMS in adapting to concept drift and optimizing ensemble diversity, offering a practical solution for real-time data stream classification.

Data stream learning is an emerging machine learning paradigm designed for environments where data arrive continuously and must be processed in real time. Unlike traditional batch learning, which assumes access to a fixed dataset, stream learning addresses the unique challenges of non-stationary distributions, bounded memory, and strict computational constraints. These challenges are increasingly relevant across domains such as IoT, finance, cybersecurity, and environmental monitoring, where timely and adaptive decision-making is essential. This tutorial introduces key concepts and techniques in data stream learning, blending foundational theory with practical demonstrations. It features CapyMOA, an open-source library that provides efficient algorithm implementations through a high-level Python API. We demonstrate the use of this tool through practical examples, with all source code available at https://github.com/adaptive-machine-learning/CapyMOA, and supporting tutorials and installation guides accessible at https://capymoa.org/.

This paper presents a machine learning-based approach for classifying the operational states in the biogas Dry Reforming (DR) reactor, focusing on catalyst activation, reaction, and irregularity detection. A key challenge in DR processes is the formation of coke, which can lead to reactor clogging. To address this, we propose incorporating the virtual drifts number, which are changes in the input data distribution, as an additional feature to enhance model performance. Different drift detection algorithms and classifiers were evaluated on a dataset comprising nine distinct DR reactions. Experimental results demonstrate that integrating virtual drift counts improves the average accuracy from 84.74% to 88.01% and the average F1 scores from 81.25% to 85.39% across all models, with RF achieving the highest performance (accuracy from 88.40% to 92.35% and F1 score from 85.95% to 91.59%). Our results highlight the potential of drift-aware features for real-time monitoring and fault detection in DR systems, offering a scalable solution to optimize reactor operations.

Abstract Gradient boosting has been extensively studied in batch learning. Recently, its streaming adaptation, Streaming Gradient Boosted Trees (Sgbt), has surpassed existing state-of-the-art random subspace and random patches methods for streaming classification under various drift scenarios. However, its application in streaming regression remains unexplored. Vanilla Sgbt with squared loss exhibits high variance when applied to streaming regression problems. To address this, we utilize bagging streaming regressors in this work to create Streaming Gradient Boosted Regression (Sgbr). Bagging streaming regressors are employed in two ways: first, as base learners within the existing Sgbt framework, and second, as an ensemble method that aggregates multiple Sgbts. Our extensive experiments on 11 streaming regression datasets, encompassing multiple drift scenarios, demonstrate that the Sgb(Oza), a variant of the first Sgbr category, significantly outperforms current state-of-the-art streaming regression methods in terms of both predictive power and computational cost.

Edge Computing (EC) has emerged as a solution to reduce energy demand and greenhouse gas emissions from digital technologies. EC supports low latency, mobility, and location awareness for delay-sensitive applications by bridging the gap between cloud computing services and end-users. Machine learning (ML) methods have been applied in EC for data classification and information processing. Ensemble learners have often proven to yield high predictive performance on data stream classification problems. Mini-batching is a technique proposed for improving cache reuse in multi-core architectures of bagging ensembles for the classification of online data streams, which benefits application speedup and reduces energy consumption. However, the original mini-batching presents limited benefits in terms of cache reuse and it hinders the accuracy of the ensembles (i.e., their capacity to detect behavior changes in data streams). In this paper, we improve mini-batching by fusing continuous training and test loops for the classification of data streams. We evaluated the new strategy by comparing its performance and energy efficiency with the original mini-batching for data stream classification using six ensemble algorithms and four benchmark datasets. We also compare mini-batching strategies with two hardware-based strategies supported by commodity multi-core processors commonly used in EC. Results show that mini-batching strategies can significantly reduce energy consumption in 95% of the experiments. Mini-batching improved energy efficiency by 96% on average and 169% in the best case. Likewise, our new mini-batching strategy improved energy efficiency by 136% on average and 456% in the best case. These strategies also support better control of the balance between performance, energy efficiency, and accuracy.

Machine Learning for Data Streams has been an important area of research since the late 1990s, and its use in industry has grown significantly over the last few years. However, there is still a gap between the cutting-edge research and the tools that are readily available, which makes it challenging for practitioners, including experienced data scientists, to implement and evaluate these methods in this complex domain. Our tutorial aims to bridge this gap with a dual focus. We will discuss important research topics, such as partially delayed labeled streams, while providing practical demonstrations of their implementation and assessment using CapyMOA, an open-source library that provides efficient algorithm implementations through a high-level Python API. Source code is available in https://github.com/adaptive-machine-learning/CapyMOA while the accompanying tutorials and installation guide are available in https://capymoa.org/.

Machine Learning (ML) is a key part of modern malware detection pipelines, but its application is not straightforward. It involves multiple practical challenges that are frequently unaddressed by the literature works. A key challenge is the heterogeneity of scenarios. Antivirus (AV) companies for instance operate under different performance constraints in the backend and in the endpoint, and with a diversity of datasets according to the country they operate in. In this paper, we evaluate the impact of these heterogeneous aspects by developing a classification pipeline for 3 datasets of 10K malware samples each collected by an AV company in the USA, Brazil, and Japan in the same period. We characterize the different requirements for these datasets and we show that a different number of features is required to reach the optimal detection rate in each scenario. We show that a global model combining the three datasets increases the detection of the three individual datasets. We propose using Federated Learning (FL) to build the global model and a distilling process to generate the local versions. We order the samples temporally to show that although retraining on concept drift detection helps recover the detection rate, only a FL approach can increase the detection rate.

Abstract Gradient Boosting is a widely-used machine learning technique that has proven highly effective in batch learning. However, its effectiveness in stream learning contexts lags behind bagging-based ensemble methods, which currently dominate the field. One reason for this discrepancy is the challenge of adapting the booster to new concept following a concept drift. Resetting the entire booster can lead to significant performance degradation as it struggles to learn the new concept. Resetting only some parts of the booster can be more effective, but identifying which parts to reset is difficult, given that each boosting step builds on the previous prediction. To overcome these difficulties, we propose Streaming Gradient Boosted Trees (Sgbt), which is trained using weighted squared loss elicited in XGBoost. Sgbt exploits trees with a replacement strategy to detect and recover from drifts, thus enabling the ensemble to adapt without sacrificing the predictive performance. Our empirical evaluation of Sgbt on a range of streaming datasets with challenging drift scenarios demonstrates that it outperforms current state-of-the-art methods for evolving data streams.

Continual learning aims to create artificial neural networks capable of accumulating knowledge and skills through incremental training on a sequence of tasks. The main challenge of continual learning is catastrophic interference, wherein new knowledge overrides or interferes with past knowledge, leading to forgetting. An associated issue is the problem of learning "cross-task knowledge," where models fail to acquire and retain knowledge that helps differentiate classes across task boundaries. A common solution to both problems is "replay," where a limited buffer of past instances is utilized to learn cross-task knowledge and mitigate catastrophic interference. However, a notable drawback of these methods is their tendency to overfit the limited replay buffer. In contrast, our proposed solution, SurpriseNet, addresses catastrophic interference by employing a parameter isolation method and learning cross-task knowledge using an auto-encoder inspired by anomaly detection. SurpriseNet is applicable to both structured and unstructured data, as it does not rely on image-specific inductive biases. We have conducted empirical experiments demonstrating the strengths of SurpriseNet on various traditional vision continual-learning benchmarks, as well as on structured data datasets. Source code made available at https://doi.org/10.5281/zenodo.8247906 and https://github.com/tachyonicClock/SurpriseNet-CIKM-23

Stream Learning (SL) attempts to learn from a data stream efficiently. A data stream learning algorithm should adapt to input data distribution shifts without sacrificing accuracy. These distribution shifts are known as ”concept drifts” in the literature. SL provides many supervised, semi-supervised, and unsupervised methods for detecting and adjusting to concept drift. On the other hand, Continual Learning (CL) attempts to preserve previous knowledge while performing well on the current concept when confronted with concept drift. In Online Continual Learning (OCL), this learning happens online. This survey explores the intersection of those two online learning paradigms to find synergies. We identify this intersection as Online Streaming Continual Learning (OSCL). The study starts with a gentle introduction to SL and then explores CL. Next, it explores OSCL from SL and OCL perspectives to point out new research trends and give directions for future research.

Malware is a major threat to computer systems and imposes many challenges to cyber security. Targeted threats, such as ransomware, cause millions of dollars in losses every year. The constant increase of malware infections has been motivating popular antiviruses (AVs) to develop dedicated detection strategies, which include meticulously crafted machine learning (ML) pipelines. However, malware developers unceasingly change their samples' features to bypass detection. This constant evolution of malware samples causes changes to the data distribution (i.e., concept drifts) that directly affect ML model detection rates, something not considered in the majority of the literature work. In this work, we evaluate the impact of concept drift on malware classifiers for two Android datasets: DREBIN (about 130K apps) and a subset of AndroZoo (about 285K apps). We used these datasets to train an Adaptive Random Forest (ARF) classifier, as well as a Stochastic Gradient Descent (SGD) classifier. We also ordered all datasets samples using their VirusTotal submission timestamp and then extracted features from their textual attributes using two algorithms (Word2Vec and TF-IDF). Then, we conducted experiments comparing both feature extractors, classifiers, as well as four drift detectors (DDM, EDDM, ADWIN, and KSWIN) to determine the best approach for real environments. Finally, we compare some possible approaches to mitigate concept drift and propose a novel data stream pipeline that updates both the classifier and the feature extractor. To do so, we conducted a longitudinal evaluation by (i) classifying malware samples collected over nine years (2009-2018), (ii) reviewing concept drift detection algorithms to attest its pervasiveness, (iii) comparing distinct ML approaches to mitigate the issue, and (iv) proposing an ML data stream pipeline that outperformed literature approaches.

In recent years, the Edge Computing (EC) paradigm has emerged as an enabling factor for developing technologies like the Internet of Things (IoT) and 5G networks, bridging the gap between Cloud Computing services and end-users, supporting low latency, mobility, and location awareness to delay-sensitive applications. An increasing number of solutions in EC have employed machine learning (ML) methods to perform data classification and other information processing tasks on continuous and evolving data streams. Usually, such solutions have to cope with vast amounts of data that come as data streams while balancing energy consumption, latency, and the predictive performance of the algorithms. Ensemble methods achieve remarkable predictive performance when applied to evolving data streams due to several models and the possibility of selective resets. This work investigates a strategy that introduces short intervals to defer the processing of mini-batches. Well balanced, our strategy can improve the performance (i.e., delay, throughput) and reduce the energy consumption of bagging ensembles to classify data streams. The experimental evaluation involved six state-of-art ensemble algorithms (OzaBag, OzaBag Adaptive Size Hoeffding Tree, Online Bagging ADWIN, Leveraging Bagging, Adaptive RandomForest, and Streaming Random Patches) applying five widely used machine learning benchmark datasets with varied characteristics on three computer platforms. As a result, our strategy can significantly reduce energy consumption in 96% of the experimental scenarios evaluated. Despite the trade-offs, it is possible to balance them to avoid significant loss in predictive performance.

Neural networks have enjoyed tremendous success in many areas over the last decade. They are also receiving more and more attention in learning from data streams, which is inherently incremental. An incremental setting poses challenges for hyperparameter optimization, which is essential to obtain satisfactory network performance. To overcome this challenge, we introduce Continuously Adaptive Neural networks for Data streams (CAND). For every prediction, CAND chooses the current best network from a pool of candidates by continuously monitoring the performance of all candidate networks. The candidates are trained using different optimizers and hyperparameters. An experimental comparison against three state-of-the-art stream learning methods, over 17 benchmark streaming datasets con-firms the competitive performance of CAND, especially on high-dimensional data. We also investigate two orthogonal heuristics for accelerating Cand,which trade-off small amounts of accuracy for significant run-time gains. We observe that training on small mini-batches yields similar accuracy to single-instance fully incremental training, even on evolving data streams.

Balancing the stability-plasticity dilemma is an omnipresent challenge in continual learning. The dilemma is that the ability of a model to learn new knowledge (plasticity) comes at the expense of the ability to remember past knowledge (stability) and vice versa. Some continual learning algorithms incorporate a constant hyper-parameter to control this trade-off. We argue that the trade-off should be dynamically tuned rather than kept constant. We propose a method to dynamically balance stability and plasticity in a semi-online and fully online manner. Our method is applicable to a range of underlying regularisation based strategies and class incremental problems. Our experiments in three continual learning benchmarks suggest that dynamic stability tuning methods can improve accuracy and decrease forgetting of continual learning strategies throughout their lifetime.

Abstract Most research in machine learning for data streams has focused on classification algorithms, whereas regression methods have received a lot less attention. This paper proposes Self-Optimising K-Nearest Leaves (SOKNL), a novel forest-based algorithm for streaming regression problems. Specifically, the Adaptive Random Forest Regression, a state-of-the-art online regression algorithm is extended like this: in each leaf, a representative data point – also called centroid – is generated by compressing the information from all instances in that leaf. During the prediction step, instead of letting all trees in the forest participate, the distances between the input instance and all centroids from relevant leaves are calculated, only k trees that possess the smallest distances are utilised for the prediction. Furthermore, we simplify the algorithm by introducing a mechanism for tuning the k values, which is dynamically and automatically optimised based on historical information. This new algorithm produces promising predictive results and achieves a superior ranking according to statistical testing when compared with several standard stream regression methods over typical benchmark datasets. This improvement incurs only a small increase in runtime and memory consumption over the basic Adaptive Random Forest Regressor.

Windows Portable Executable files can be malformed for malicious purposes. There are many ways and tricks to circumvent standard security detection and protection measures. For example, one can bypass Windows Defender Firewall by creating a writable file in a user's temporary folder whose filename look like a legitimate process (e.g. svchost.exe, chrome32.exe, and dllhost32.exe) and executing them without user intervention. In this work, we leverage static properties and dynamic behaviour analysis for malware classification. For dynamic analysis, information is retrieved from the Falcon Sandbox malware website. On top of that, we also run malware in a virtualised Windows 10 environment to analyse memory dumps and generate even more features that may capture potential malicious behaviour. Three different classifiers are analysed in our empirical experiments: random forests, gradient boosting, and neural networks. The combination of static and dynamic features consistently yields a higher F1-score for every model compared to the same model trained using only static or dynamic features. The best models achieve F1-scores of up to 98.9%.

River is a machine learning library for dynamic data streams and continual learning. It provides multiple state-of-the-art learning methods, data generators/transformers, performance metrics and evaluators for different stream learning problems. It is the result from the merger of two popular packages for stream learning in Python: Creme and scikit-multiflow. River introduces a revamped architecture based on the lessons learnt from the seminal packages. River's ambition is to be the go-to library for doing machine learning on streaming data. Additionally, this open source package brings under the same umbrella a large community of practitioners and researchers. The source code is available at https://github.com/online-ml/river.

Streaming Machine Learning (SML) studies single-pass learning algorithms that update their models one data item at a time given an unbounded and often non-stationary flow of data (a.k.a., in presence of concept drift). Online class imbalance learning is a branch of SML that combines the challenges of both class imbalance and concept drift. In this paper, we investigate the binary classification problem of rebalancing an imbalanced stream of data in the presence of concept drift, accessing one sample at a time. We propose Continuous Synthetic Minority Oversampling Technique (C-SMOTE), a novel rebalancing meta-strategy to pipeline with SML classification algorithms. C-SMOTE is inspired by the popular SMOTE algorithm but operates continuously. We benchmark C-SMOTE pipelines on ten different groups of data streams. We bring empirical evidence that models learnt with C-SMOTE pipelines outperform models trained on imbalanced data stream without losing the ability to deal with concept drifts. Moreover, we show that they outperform other stream balancing techniques from the literature.

Mining high-dimensional data streams poses a fundamental challenge to machine learning as the presence of high numbers of attributes can remarkably degrade any mining task's performance. In the past several years, dimension reduction (DR) approaches have been successfully applied for different purposes (e.g., visualization). Due to their high-computational costs and numerous passes over large data, these approaches pose a hindrance when processing infinite data streams that are potentially high-dimensional. The latter increases the resource-usage of algorithms that could suffer from the curse of dimensionality. To cope with these issues, some techniques for incremental DR have been proposed. In this paper, we provide a survey on reduction approaches designed to handle data streams and highlight the key benefits of using these approaches for stream mining algorithms.

Ensemble-based methods are one of the most often used methods in the classification task that have been adapted to the stream setting because of their high learning performance achievement. For instance, Adaptive Random Forests (ARF) is a recent ensemble method for evolving data streams that proved to be of a good predictive performance but, as all ensemble methods, it suffers from a severe drawback related to the high computational demand which prevents it from being efficient and further exacerbates with high-dimensional data. In this context, the application of a dimensionality reduction technique is crucial while processing the Internet of Things (IoT) data stream with ultrahigh dimensionality. In this paper, we aim to alleviate this deficiency and improve ARF performance, so we introduce the CS-ARF approach that uses Compressed Sensing (CS) as an internal pre-processing task, to reduce the dimensionality of data before starting the learning process, that will potentially lead to a meaningful improvement in memory usage. Experiments on various datasets show the high classification performance of our CS-ARF approach compared against current state-of-the-art methods while reducing resource usage.

An ensemble of learners tends to exceed the predictive performance of individual learners. This approach has been explored for both batch and online learning. Ensembles methods applied to data stream classification were thoroughly investigated over the years, while their regression counterparts received less attention in comparison. In this work, we discuss and analyze several techniques for generating, aggregating, and updating ensembles of regressors for evolving data streams. We investigate the impact of different strategies for inducing diversity into the ensemble by randomizing the input data (resampling, random subspaces and random patches). On top of that, we devote particular attention to techniques that adapt the ensemble model in response to concept drifts, including adaptive window approaches, fixed periodical resets and randomly determined windows. Extensive empirical experiments show that simple techniques can obtain similar predictive performance to sophisticated algorithms that rely on reactive adaptation (i.e., concept drift detection and recovery).

For many streaming classification tasks, the ground truth labels become available with a non-negligible latency. Given this delayed labelling setting, after the instance data arrives and before its true label is known, the online classifier model may change. Hence, the initial prediction can be replaced with additional periodic predictions gradually produced before the true label becomes available. The quality of these predictions may largely vary. Thus, the question arises of how to summarise the performance of these models when multiple predictions for a single instance are made due to delayed labels.In this study, we aim to provide intuitive performance measures summarising the performance of multiple predictions made for individual instances before their true labels arrive. Particular attention is paid to the fact that under the delayed label setting, the emphasis placed on the quality of initial predictions can vary depending on problem needs. The intermediate performance measures we propose complement existing initial and test-then-train performance evaluation when verification latency is observed. Results provided for both real and synthetic datasets show that the new measures can be used to easily rank methods in terms of their ability to produce and refine predictions before the true labels arrive.

Abstract A large portion of the stream mining studies on classification rely on the availability of true labels immediately after making predictions. This approach is well exemplified by the test-then-train evaluation, where predictions immediately precede true label arrival. However, in many real scenarios, labels arrive with non-negligible latency. This raises the question of how to evaluate classifiers trained in such circumstances. This question is of particular importance when stream mining models are expected to refine their predictions between acquiring instance data and receiving its true label. In this work, we propose a novel evaluation methodology for data streams when verification latency takes place, namely continuous re-evaluation. It is applied to reference data streams and it is used to differentiate between stream mining techniques in terms of their ability to refine predictions based on newly arriving instances. Our study points out, discusses and shows empirically the importance of considering the delay of instance labels when evaluating classifiers for data streams.

Assigning scores to individual features is a popular method for estimating the relevance of features in supervised learning. An accurate feature score estimation provides essential insights in sensitive domains, which is decisive to explain how features influence a given decision, contributing to the interpretability of the model. Learning from streaming data adds several challenges to machine learning tasks, including limited resources and changes to the underlying data distribution (i.e., evolving data streams). In this work, we introduce and analyze methods to efficiently estimate the Mean Decrease in Impurity (MDI) and COVER measures using ensembles of incremental decision trees. To achieve current scores in evolving data streams, we employ tree-ensembles that incorporate active drift detection. Experimental results show how MDI and COVER can be used to track the feature scores when their importance to the ensemble model shift over time. On top of that, we present the impact on the feature scores when the learning problem includes a non-negligible verification latency for the arrival of the labels. We also present a counter-intuitive experiment using a standard benchmark dataset where the feature scores correctly illustrate the importance of two features to the ensemble model. However, these features are prioritized due to biased split decisions, and in their absence, the model increases in predictive performance. We conclude that the presented measures can be used to understand the impact of features in the ensemble model better, still, such measures should be used with caution as they are limited by the underlying tree building and ensemble model biases.

Machine learning algorithms for data streams usually suppose that all data examples available for learning are strictly labeled. Unfortunately, in real-world scenarios, data examples are not always labeled. Semi-supervised learning is a challenging task to learn using labeled and unlabeled data at the same time. It is especially relevant in the context of data streams, where the data is generated in real-time, and the labels may be missing due to various factors (e.g., network delay, errors during the communication between sensors, expensive labeling process, and others). In this paper, we present two novel approaches to handle missing labels for classification learning in data streams, namely cluster-and-label and self-training. We discuss the strengths and weaknesses of each solution to establish a baseline to evaluate semi-supervised learning techniques in data streams. These methods are implemented inside the MOA (Massive Online Analysis) open-source software as an internal benchmark component, to help researchers to run experimental comparisons on semi-supervised learning on data streams easily.

Ensemble methods are a popular choice for learning from evolving data streams. This popularity is due to (i) the ability to simulate simple, yet, successful ensemble learning strategies, such as bagging and random forests; (ii) the possibility of incorporating drift detection and recovery in conjunction to the ensemble algorithm; (iii) the availability of efficient incremental base learners, such as Hoeffding Trees. In this work, we introduce the Streaming Random Patches (SRP) algorithm, an ensemble method specially adapted to stream classification which combines random subspaces and online bagging. We provide theoretical insights and empirical results illustrating different aspects of SRP. In particular, we explain how the widely adopted incremental Hoeffding trees are not, in fact, unstable learners, unlike their batch counterparts, and how this fact significantly influences ensemble methods design and performance. We compare SRP against state-of-the-art ensemble variants for streaming data in a multitude of datasets. The results show how SRP produce a high predictive performance for both real and synthetic datasets. Besides, we analyze the diversity over time and the average tree depth, which provides insights on the differences between local subspace randomization (as in random forest) and global subspace randomization (as in random subspaces).

The large volume of data generated by computer networks, smartphones, wearables and a wide range of sensors, which produce real-time data, are only useful if they can be efficiently processed so that individuals can make timely decisions based on them. In this context, machine learning techniques are widely used. While it performs better than humans in such tasks, every machine learning algorithm has a certain intrinsic bias, which means they assume that the data have specific characteristics, such as having a balanced distribution between classes. As many real-world applications present imbalanced traits in their data, this topic is gaining repercussion over time. In this work, we present the Adaptive Random Forest with Resampling (ARFRE), which is a classifier designed to deal with imbalanced datasets. ARFREresample the instances based on the current class label distribution. We show through a set of extensive experiments on seven datasets that the proposed method can considerably improve the performance of the minority class(es) while avoiding degrading the performance in the majority class. On top of that, ARFREis more efficient regarding execution time in comparison to the standard ARF algorithm.

Trust mechanisms are considered the logical protection of software systems, preventing malicious people from taking advantage or cheating others. Although these concepts are widely used, most applications in this field do not consider affective aspects to aid in trust computation. Researchers of Psychology, Neurology, Anthropology, and Computer Science argue that affective aspects are essential to human's decision-making processes. So far, there is a lack of understanding about how these aspects impact user's trust, particularly when they are inserted in an evaluation system. In this paper, we propose a trust model that accounts for personality using three personality models: Big Five, Needs, and Values. We tested our approach by extracting personality aspects from texts provided by two online human-fed evaluation systems and correlating them to reputation values. The empirical experiments show statistically significant better results in comparison to non-personality-wise approaches.

The financial market is one of the major consumers of data mining techniques, and the main reason is their efficiency to analyze complex data. One important trait shared between most financial applications is class imbalance. Since traditional classification methods assume nearly balanced classes and equal misclassification costs, they usually fail to deal with imbalanced data. However, in financial contexts, problems are usually imbalanced, and instances from the minority class are known for deficits of millions of dollars every year, e.g., credit card frauds, money laundering transactions and so forth. Over the years, several techniques for dealing with class imbalance have been developed, such as sampling techniques and algorithm adaptations. In this study, we analyze how different sampling techniques impact the performance of different classification systems on financial applications. Results show that, for the given datasets, sampling techniques allow the improvement of prediction performance of the minority class while also improving overall classification rates. Nevertheless, their use often deteriorates the performance in predicting the majority class.

Peer-to-peer (P2P) lending is a global trend of financial markets that allow individuals to obtain and concede loans without having financial institutions as a strong proxy. As many real-world applications, P2P lending presents an imbalanced characteristic, where the number of creditworthy loan requests is much larger than the number of non-creditworthy ones. In this work, we wrangle a real-world P2P lending data set from Lending Club, containing a large amount of data gathered from 2007 up to 2016. We analyze how supervised classification models and techniques to handle class imbalance impact creditworthiness prediction rates. Ensembles, cost-sensitive and sampling methods are combined and evaluated along logistic regression, decision tree, and bayesian learning schemes. Results show that, in average, sampling techniques outperform ensembles and cost sensitive approaches.

The ever increasing data generation confronts both practitioners and researchers on handling massive and sequentially generated amounts of information, the so-called data streams. In this context, a lot of effort has been put on the extraction of useful patterns from streaming scenarios. Learning from data streams embeds a variety of problems, and by far, the most challenging is concept drift, i.e. changes in data distribution. In this paper, we focus on a specific type of drift uncommonly assessed in the literature: feature drifts. Feature drifts occur whenever a subset of features becomes, or ceases to be, relevant to the concept to be learned. We propose and review several feature drifting data stream generators and use them to benchmark state-of-the-art data stream classification algorithms and their combination with drift detectors. Results show that, although drift detectors enable slight quicker recovery to feature drifts, best results are obtained by Hoeffding Adaptive Tree, the only learner that performs dynamic feature selection as streams progress.

Extracting useful knowledge from data streams is problematic, mainly due to changes in their data distribution, a phenomenon named concept drift. Recently, studies have shown that most of existing algorithms for learning from data streams do not encompass techniques for a specific kind of drift: feature drifts. Feature drifts occur when features become, or cease to be, relevant to the learning task. In this paper, we propose an extension to the k-nearest neighbor classifier, so its distances' computations are weighted according to their current discriminative power. On our proposal, the discriminative power of features is given by entropy, which is swiftly computed over a sliding window. Empirical evidence shows that our approach is able to overcome several existing algorithms in accuracy and feature drift adaptation, while at the expense of bounded processing time and memory space.

Mining data streams is of the utmost importance due to its appearance in many real-world situations, such as: sensor networks, stock market analysis and computer networks intrusion detection systems. Data streams are, by definition, potentially unbounded sequences of data that arrive intermittently at rapid rates. Extracting useful knowledge from data streams embeds virtually all problems from conventional data mining with the addition of single-pass real-time processing within limited time and memory space. Additionally, due to its ephemeral nature, it is expected that streams undergo changes in its data distribution denominated concept drifts. In this work, we focus on one specific kind of concept drift that has not been extensively addressed in the literature, namely feature drift. A feature drift happens when changes occur in the set of features, such that a subset of features become, or cease to be, relevant to the learning problem. Specifically, changes in the relevance of features directly imply modifications in the decision boundary to be learned, thus the learner must detect and adapt to according to it. Timely detection and recover from feature drifts is a challenging task that can be modeled after a dynamic feature selection problem. In this paper we survey existing work on dynamic feature selection for data streams that acts either implicitly or explicitly. We conclude that there is a need for future research in this area, which we highlight as future research directions.

This work encompasses the development of a new ensemble classifier that uses a Social Network abstraction for Data Stream Classification, namely the Social Adaptive Ensemble (SAE). In the context of data stream classification, concept drift is considered one of the most difficult and important issues to be addressed. Ensemble classifiers can be successfully applied to data streams as long as the ensemble efficiently adapts itself in the occurrence of a concept drift. SAE algorithm inherits strategies from other ensemble methods, such as Online Bagging [4] and DWM [2], and merge these with the notion of connectivity between similar classifiers w.r.t. their individual predictions. The relational data obtained through measuring similarities between classifiers is used to arrange ensemble members in a social network structure that allows us to identify subgroups (subnetworks) of highly similar classifiers. Being able to identify similar classifiers allows us to implement a combination strategy that first combines predictions within similar classifiers and later combines these into the final prediction. Moreover, this combination strategy assigns more weight to recently added classifiers predictions during concept drifts, since these are dissimilar to all other existing classifiers. The similarity between classifiers is also used to identify and remove redundant classifiers. This effectively saves systems resources and sometimes improves accuracy. We present empirical experiments with synthetic data streams containing abrupt, gradual and no drift showing that SAE is a valid option for stream classification, especially when data stream characteristics (e.g. presence of abrupt drifts) are previously unknown and system resources, such as CPU time and memory space, are a concern.