This study explores dataset complexity estimation and its relationship with classification performance. Widely-used measures like the k-Disagreeing Neighbors (kDN) are limited by their reliance on a fixed number of neighbors ( k ) and their discrete output, which can lead to coarse estimations. To address these issues, we introduce Dynamic Disagreeing Neighbors (DDN), a measure that incorporates dynamic, density-aware neighborhoods and distance-based weighting to provide a smoother, more flexible complexity estimation. We conduct a large-scale empirical study on 65 binary datasets, comparing DDN against kDN and other state-of-the-art measures, analyzing the impact of the k parameter and class imbalance on the alignment with classifier performance. Our results show that DDN provides a more robust and stable correspondence with performance. Furthermore, we demonstrate that in a predictive modeling task, a regression model using DDN as a feature is significantly more accurate at estimating dataset performance than models using other complexity measures. These findings not only deepen our understanding of complexity estimation but also pave the way for more informed classifier selection and data preprocessing strategies.

Abstract Complexity measures aim to characterize the underlying complexity of supervised data. These measures tackle factors hindering the performance of Machine Learning (ML) classifiers like overlap, density, linearity, etc. The state-of-the-art has mainly focused on the dataset perspective of complexity, i.e., offering an estimation of the complexity of the whole dataset. Recently, the instance perspective has also been addressed. In this paper, the hostility measure, a complexity measure offering a multi-level (instance, class, and dataset) perspective of data complexity is proposed. The proposal is built by estimating the novel notion of hostility: the difficulty of correctly classifying a point, a class, or a whole dataset given their corresponding neighborhoods. The proposed measure is estimated at the instance level by applying the k-means algorithm in a recursive and hierarchical way, which allows to analyze how points from different classes are naturally grouped together across partitions. The instance information is aggregated to provide complexity knowledge at the class and the dataset levels. The validity of the proposal is evaluated through a variety of experiments dealing with the three perspectives and the corresponding comparative with the state-of-the-art measures. Throughout the experiments, the hostility measure has shown promising results and to be competitive, stable, and robust.

Similarity search based on a distance function in metric spaces is a fundamental problem for many applications. Queries for similar objects lead to the well-known machine learning task of nearest-neighbours identification. Many data indexing strategies, collectively known as Metric Access Methods (MAM), have been proposed to speed up these queries. Moreover, since exact approaches to solving similarity queries can be complex and time-consuming, alternative options have emerged to reduce query execution time, such as returning approximate results or resorting to distributed computing platforms. In this paper, we introduce MASK (Multilevel Approximate Similarity search with k-means), an unconventional application of the k-means algorithm as the foundation of a multilevel index structure for approximate similarity search suitable for metric spaces. We show that this method leverages inherent properties of k-means for this purpose, like representing high-density data areas with fewer prototypes. An implementation of this new indexing procedure is evaluated using a synthetic dataset and two real-world datasets in high-dimensional and high-sparsity spaces. Experimental tests show that MASK performs better than alternative algorithms for approximate similarity search. Results are promising and underpin the applicability of this novel indexing method in multiple domains.

Customer acquisition is an issue that continues to receive attention from companies worldwide.Various marketing campaigns using psychological methodologies have been designed to address this issue.However, once a campaign is launched, it is highly complicated to detect which sets of customers are most likely to purchase an offered product.This fact is key since it allows companies to focus their efforts on specific clients and discard others.Several selection techniques have been implemented, but most of them are usually very demanding in terms of time and human resources for the companies.Artificial Intelligence techniques appear to help to simplify the process.Thus, companies have started to use Machine Learning (ML) models trained to efficiently detect those clients with certain proneness to purchase.Toward this goal, this paper presents a novel purchase propensity detection ML system based on Sentiment Analysis techniques able to consider customer comments regarding the offered products.The tourist domain was selected for the case study, where the obtained product was successfully embedded in an initial prototype.

Abstract Several performance metrics are currently available to evaluate the performance of Machine Learning (ML) models in classification problems. ML models are usually assessed using a single measure because it facilitates the comparison between several models. However, there is no silver bullet since each performance metric emphasizes a different aspect of the classification. Thus, the choice depends on the particular requirements and characteristics of the problem. An additional problem arises in multi-class classification problems, since most of the well-known metrics are only directly applicable to binary classification problems. In this paper, we propose the General Performance Score (GPS) , a methodological approach to build performance metrics for binary and multi-class classification problems. The basic idea behind GPS is to combine a set of individual metrics, penalising low values in any of them. Thus, users can combine several performance metrics that are relevant in the particular problem based on their preferences obtaining a conservative combination. Different GPS -based performance metrics are compared with alternatives in classification problems using real and simulated datasets. The metrics built using the proposed method improve the stability and explainability of the usual performance metrics. Finally, the GPS brings benefits in both new research lines and practical usage, where performance metrics tailored for each particular problem are considered.

Voice transmission is no longer the main usage of mobile phones. Data transmissions, in particular Internet access, are very common actions that we might perform with these devices. However, the spectacular growth of the mobile data demand in 5 G mobile communication systems leads to a reduction of the resources assigned to each device. Therefore, to avoid situations in which the Quality of Experience (QoE) would be negatively affected, an automated system for degradation detection of video streaming is proposed. This approach is named QoE Management for Mobile Users (QoEMU). QoEMU is composed of several modules to perform a real-time analysis of the network traffic, select a mitigation action according to the information of the traffic and to some predefined policies, and apply these actions. In order to perform such tasks, the best Key Performance Indicators (KPIs) for a given set of video traces are selected. A QoE Model is trained to define a global QoE for the set of traces. When an alert regarding degradation in the quality appears, a proper mitigation plan is activated to mitigate this situation. The performance of QoEMU has been evaluated over a degradation situation experiments with different video users.

In this paper, we investigate the problem of estimating high-density regions from univariate or multivariate data samples. We estimate minimum volume sets, whose probability is specified in advance, known in the literature as density contour clusters. This problem is strongly related to One-Class Support Vector Machines (OCSVM). We propose a new method to solve this problem, the One-Class Neighbor Machine (OCNM) and we show its properties. In particular, the OCNM solution asymptotically converges to the exact minimum volume set prespecified. Finally, numerical results illustrating the advantage of the new method are shown.