MLSMOTE: Approaching imbalanced multilabel learning through synthetic instance generation

Charte, Francisco; Rivera, Antonio J.; Jesus, María José del; Herrera, Francisco

doi:10.1016/j.knosys.2015.07.019

Cited by 192 publications

(113 citation statements)

References 54 publications

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“…However, little attention was paid to imbalanced learning in their context, despite the fact that these areas suffer from it. In multi-label learning so far measures of imbalance and SMOTE-based oversampling approach have been proposed [8]. In multi-instance learning cost-sensitive and standard resampling have been used to counter skewed distributions with regard to number of bags and number of instances within bags [38,59].…”

Section: Multi-label and Multi-instance Imbalanced Classificationmentioning

confidence: 99%

Learning from imbalanced data: open challenges and future directions

2016

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Despite more than two decades of continuous development learning from imbalanced data is still a focus of intense research. Starting as a problem of skewed distributions of binary tasks, this topic evolved way beyond this conception. With the expansion of machine learning and data mining, combined with the arrival of big data era, we have gained a deeper insight into the nature of imbalanced learning, while at the same time facing new emerging challenges. Data-level and algorithm-level methods are constantly being improved and hybrid approaches gain increasing popularity. Recent trends focus on analyzing not only the disproportion between classes, but also other difficulties embedded in the nature of data. New real-life problems motivate researchers to focus on computationally efficient, adaptive and real-time methods. This paper aims at discussing open issues and challenges that need to be addressed to further develop the field of imbalanced learning. Seven vital areas of research in this topic are identified, covering the full spectrum of learning from imbalanced data: classification, regression, clustering, data streams, big data analytics and applications, e.g., in social media and computer vision. This paper provides a discussion and suggestions concerning lines of future research for each of them.

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Section: Multi-label and Multi-instance Imbalanced Classificationmentioning

confidence: 99%

Learning from imbalanced data: open challenges and future directions

2016

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“…Despite its inability to completely eliminate them, it is a good indicative that investing in similar techniques can mitigate the label prediction problems. Thus, the investigation of other MLC techniques for data oversampling (Charte et al, ; Charte et al, ) and attribute selection (Pereira et al, ) as solutions to the MLP and WLP problems are suggested for future work.…”

Section: Resultsmentioning

confidence: 99%

“…The use of these three measures can quantify and assess the occurrence or absence of the previously mentioned label problems. Some works related to label imbalanced (Charte, Rivera, del Jesús, & Herrera, 2015;Charte et al, 2017) tried to increase the multilabel measures, especially the macro-F1. However, they did not investigate the occurrence of these problems.…”

Section: Label Prediction Problemsmentioning

confidence: 99%

Enhancing multilabel classification for food truck recommendation

2018

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Food trucks are a widely popular fast food restaurant alternative, whose differentiating factor is their proximity to customers. Their popularity has stimulated the expansion of available options, which now includes several different types of cuisines, consequently making the choice by customers a challenging issue. From data obtained via a market research, in which hundreds of participants provided their food truck preferences, this paper focuses on the problem of food truck recommendation using a multilabel approach. In particular, it investigates how to improve the recommendation task regarding a previous work, where some labels have never been predicted. In order to address this problem, different alternatives were investigated. One of these alternatives, the Ensemble of Single Label, proposed in this paper, was able to reduce it. Despite its simplicity, good predictive results were obtained when they were used in the investigated task. Among other benefits, all labels were correctly predicted at least for few instances.

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“…This peculiarity is known as imbalance [6], and it has been profoundly studied in traditional classification. In the context of MLC, several proposals to deal with imbalanced MLDs [7][8][9][10][11][12][13][14][15] have been made lately. Despite these efforts, there are still some aspects regarding imbalanced learning in MLC that would need additional analysis.…”

Section: Introductionmentioning

confidence: 99%

Dealing with difficult minority labels in imbalanced mutilabel data sets

et al. 2019

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Multilabel classification is an emergent data mining task with a broad range of real world applications. Learning from imbalanced multilabel data is being deeply studied latterly, and several resampling methods have been proposed in the literature. The unequal label distribution in most multilabel datasets, with disparate imbalance levels, could be a handicap while learning new classifiers. In addition, this characteristic challenges many of the existent preprocessing algorithms. Furthermore, the concurrence between imbalanced labels can make harder the learning from certain labels. These are what we call difficult labels. In this work, the problem of difficult labels is deeply analyzed, its influence in multilabel classifiers is studied, and a novel way to solve this problem is proposed. Specific metrics to assess this trait in multilabel datasets, called SCUMBLE (Score of ConcUrrence among iMBalanced LabEls) and SCUMBLELbl, are presented along with REMEDIAL (REsampling MultilabEl datasets by Decoupling highly ImbAlanced Labels), a new algorithm aimed to relax label concurrence. How to deal with this problem using the R mldr package is also outlined. (Francisco Charte), arivera@ujaen.es (Antonio J. Rivera), mjjesus@ujaen.es (María J. del Jesus), herrera@ugr.es (Francisco Herrera)

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MLSMOTE: Approaching imbalanced multilabel learning through synthetic instance generation

Cited by 192 publications

References 54 publications

Learning from imbalanced data: open challenges and future directions

Learning from imbalanced data: open challenges and future directions

Enhancing multilabel classification for food truck recommendation

Dealing with difficult minority labels in imbalanced mutilabel data sets

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