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.

A multi-label classifier estimates the binary label state (relevant/irrelevant) for each of a set of concept labels, for a given instance. Probabilistic multi-label classifiers provide a distribution over all possible labelset combinations of such label states (the powerset of labels), from which we can provide the best estimate by selecting the labelset corresponding to the largest expected accuracy. Providing confidence for predictions is important for real-world application of multi-label models, which provides the practitioner with a sense of the correctness of the prediction. It has been thought that the probability of the chosen labelset is a good measure of the confidence of the prediction, but multi-label accuracy can be measured in many ways and so confidence should align with the expected accuracy of the evaluation method. In this article, we investigate the effectiveness of seven candidate functions for estimating multi-label expected accuracy conditioned on the labelset distribution and the evaluation method. We found most correlate to expected accuracy and have varying levels of robustness. Further, we found that the candidate functions provide high expected accuracy estimates for Hamming similarity, but a combination of the candidates provided an accurate estimate of expected accuracy for Jaccard index and Exact match.

Some of the most effective influential spreader detection algorithms are unstable to small perturbations of the network structure. Inspired by bagging in Machine Learning, we propose the first Perturb and Combine (P&C) procedure for networks. It (1) creates many perturbed versions of a given graph, (2) applies a node scoring function separately to each graph, and (3) combines the results. Experiments conducted on real-world networks of various sizes with the k-core, generalized k-core, and PageRank algorithms reveal that P&C brings substantial improvements. Moreover, this performance boost can be obtained at almost no extra cost through parallelization. Finally, a bias-variance analysis suggests that P&C works mainly by reducing bias, and that therefore, it should be capable of improving the performance of all vertex scoring functions, including stable ones. An extended version of this paper is provided by [1].

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).

Data ubiquity highlights the need of efficient and adaptable data-driven solutions. In this paper, we present FAST AND SLOW LEARNING (FSL), a novel unified framework that sheds light on the symbiosis between batch and stream learning. FSL works by employing Fast (stream) and Slow (batch) Learners, emulating the mechanisms used by humans to make decisions. We showcase the applicability of FSL on the task of classification by introducing the FAST AND SLOW CLASSIFIER (FSC). A Fast Learner provides predictions on the spot, continuously updating its model and adapting to changes in the data. On the other hand, the Slow Learner provides predictions considering a wider spectrum of seen data, requiring more time and data to create complex models. Once that enough data has been collected, FSC trains the Slow Learner and starts tracking the performance of both learners. A drift detection mechanism triggers the creation of new Slow models when the current Slow model becomes obsolete. FSC selects between Fast and Slow Learners according to their performance on new incoming data. Test results on real and synthetic data show that FSC effectively drives the positive interaction of stream and batch models for learning from evolving data streams.

We propose the task of set labelling. Starting from some examples members of a set, set labelling tries to infer the most appropriate labels for the given set. For this work, we consider sets of words. We illustrate the task and a possible solution with an application to the classification of cosmetic products and hotels. The novel solution proposed in this research is to incorporate a multi-label classifier trained from the labeled datasets. We use vectorization of the description of the seeds as input to the classifier as well as labels assigned to it. Given a previously unseen data, the trained classifier returns a ranked list of candidate labels (i.e., additional seeds) for the set. These results could then be used to infer the labels for the set. We implement our proposed solution to the classification of cosmetic products and hotels. We show that the solution is effective and efficient.

Real-time mining of evolving data streams involves new challenges when targeting today's application domains such as the Internet of the Things: increasing volume, velocity and volatility requires data to be processed on-the-fly with fast reaction and adaptation to changes. This paper presents a high performance scalable design for decision trees and ensemble combinations that makes use of the vector SIMD and multicore capabilities available in modern processors to provide the required throughput and accuracy. The proposed design offers very low latency and good scalability with the number of cores on commodity hardware when compared to other state-of-the art implementations. On an Intel i7-based system, processing a single decision tree is 6× faster than MOA (Java), and 7× faster than StreamDM (C++), two well-known reference implementations. On the same system, the use of the 6 cores (and 12 hardware threads) available allow to process an ensemble of 100 learners 85× faster that MOA while providing the same accuracy. Furthermore, our solution is highly scalable: on an Intel Xeon socket with large core counts, the proposed ensemble design achieves up to 16× speedup when employing 24 cores with respect to a single threaded execution.

The evaluation of classifiers in data streams is fundamental so that poorly-performing models can be identified, and either improved or replaced by better-performing models. This is an increasingly relevant and important task as stream data is generated from more sources, in real-time, in large quantities, and is now considered the largest source of big data. Both researchers and practitioners need to be able to effectively evaluate the performance of the methods they employ. However, there are major challenges for evaluation in a stream. Instances arriving in a data stream are usually time-dependent, and the underlying concept that they represent may evolve over time. Furthermore, the massive quantity of data also tends to exacerbate issues such as class imbalance. Current frameworks for evaluating streaming and online algorithms are able to give predictions in real-time, but as they use a prequential setting, they build only one model, and are thus not able to compute the statistical significance of results in real-time. In this paper we propose a new evaluation methodology for big data streams. This methodology addresses unbalanced data streams, data where change occurs on different time scales, and the question of how to split the data between training and testing, over multiple models.

The area of multi-label classification has rapidly developed in recent years. It has become widely known that the baseline binary relevance approach can easily be outperformed by methods which learn labels together. A number of methods have grown around the label power set approach, which models label combinations together as class values in a multi-class problem. We describe the label-power set-based solutions under a general framework of meta-labels and provide some theoretical justification for this framework which has been lacking, explaining how meta-labels essentially allow a random projection into a space where non-linearities can easily be tackled with established linear learning algorithms. The proposed framework enables comparison and combination of related approaches to different multi-label problems. We present a novel model in the framework and evaluate it empirically against several high-performing methods, with respect to predictive performance and scalability, on a number of datasets and evaluation metrics. This deployment obtains competitive accuracy for a fraction of the computation required by the current meta-label methods for multi-label classification.

The multi-dimensional classification problem is a generalization of the recently-popularized task of multi-label classification, where each data instance is associated with multiple class variables. There has been relatively little research carried out specific to multi-dimensional classification and, although one of the core goals is similar (modeling dependencies among classes), there are important differences; namely a higher number of possible classifications. In this paper we present method for multi-dimensional classification, drawing from the most relevant multi-label research, and combining it with important novel developments. Using a fast method to model the conditional dependence between class variables, we form super-class partitions and use them to build multi-dimensional learners, learning each super-class as an ordinary class, and thus explicitly modeling class dependencies. Additionally, we present a mechanism to deal with the many class values inherent to super-classes, and thus make learning efficient. To investigate the effectiveness of this approach we carry out an empirical evaluation on a range of multi-dimensional datasets, under different evaluation metrics, and in comparison with high-performing existing multi-dimensional approaches from the literature. Analysis of results shows that our approach offers important performance gains over competing methods, while also exhibiting tractable running time.

This paper presents a pruned sets method (PS) for multi-label classification. It is centred on the concept of treating sets of labels as single labels. This allows the classification process to inherently take into account correlations between labels. By pruning these sets, PS focuses only on the most important correlations, which reduces complexity and improves accuracy. By combining pruned sets in an ensemble scheme (EPS), new label sets can be formed to adapt to irregular or complex data. The results from experimental evaluation on a variety of multi-label datasets show that [E]PS can achieve better performance and train much faster than other multi-label methods.