A study on combining dynamic selection and data preprocessing for imbalance learning

Roy, Anandarup; Cruz, Rafael M. O.; Sabourin, Robert; Cavalcanti, George D. C.

doi:10.1016/j.neucom.2018.01.060

Cited by 105 publications

(56 citation statements)

References 47 publications

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“…Thus, making it easier to replicate the results of this paper. The process of creating the dynamic selection dataset (DSEL) was guided by the experiments conducted in [22].…”

Section: Discussionmentioning

confidence: 99%

“…This could be explained by the fact the Random Balance was proposed to deal specifically with small sized and imbalanced data [42], which comprises the 64 datasets in this study. Moreover, this technique achieved the state-of-theart performance for such datasets in several comparative studies [29,22]. Hence, the FKNE++ is competitive with the state-of-the-art methods for dealing with small sized and imbalanced datasets.…”

Section: Comparison Among Different Scenariosmentioning

confidence: 95%

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FIRE-DES++: Enhanced online pruning of base classifiers for dynamic ensemble selection

Cruz

Oliveira

Cavalcanti

et al. 2019

Pattern Recognition

Self Cite

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Dynamic Ensemble Selection (DES) techniques aim to select one or more competent classifiers for the classification of each new test sample. Most DES techniques estimate the competence of classifiers using a given criterion over the region of competence of the test sample, usually defined as the set of nearest neighbors of the test sample in the validation set. Despite being very effective in several classification tasks, DES techniques can select classifiers that classify all samples in * Corresponding author datasets. Experimental results show that FIRE-DES++ increases the classification performance of all DES techniques considered in this work, outperforming FIRE-DES with 7 out of the 8 DES techniques, and outperforming state-of-the-art DES frameworks.

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“…Thus, making it easier to replicate the results of this paper. The process of creating the dynamic selection dataset (DSEL) was guided by the experiments conducted in [22].…”

Section: Discussionmentioning

confidence: 99%

Section: Comparison Among Different Scenariosmentioning

confidence: 95%

FIRE-DES++: Enhanced online pruning of base classifiers for dynamic ensemble selection

Cruz

Oliveira

Cavalcanti

et al. 2019

Pattern Recognition

Self Cite

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“…The use of sampling techniques in conjunction with ensemble learning is usually more straightforward than combining them to boosting because the bagging method does not require to compute weights and adapt the update formulas of weights during the learning process [52]. Many studies integrating random undersampling into bagging may be explored in [53].…”

Section: Overview Of the Methodsmentioning

confidence: 99%

Evaluation of Sampling-Based Ensembles of Classifiers on Imbalanced Data for Software Defect Prediction Problems

Khuat

2020

SN COMPUT. SCI.

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Defect prediction in software projects plays a crucial role to reduce quality-based risk and increase the capability of detecting faulty program modules. Hence, classification approaches to anticipate software defect proneness based on static code characteristics have become a hot topic with a great deal of attention in recent years. While several novel studies show that the use of a single classifier causes the performance bottleneck, ensembles of classifiers might effectively enhance classification performance compared to a single classifier. However, the class imbalance property of software defect data severely hinders the classification efficiency of ensemble learning. To cope with this problem, resampling methods are usually combined into ensemble models. This paper empirically assesses the importance of sampling with regard to ensembles of various classifiers on imbalanced data in software defect prediction problems. Extensive experiments with the combination of seven different kinds of classification algorithms, three sampling methods, and two balanced data learning schemata were conducted over ten datasets. Empirical results indicated the positive effects of combining sampling techniques and the ensemble learning model on the performance of defect prediction regarding datasets with imbalanced class distributions.

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“…As shown in [6], a diverse ensemble can better cope with imbalanced distribution. In particular, Dynamic selection (DS) techniques is seen as an alternative to deal with multi-class imbalance as it explores the local competence of each base classifier according to each new query sample [7,2,8]. Only the base classifiers that attained a certain competence level, in the given local region, are selected to predict the label of the query sample.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Ensemble Selection and Data Preprocessing for Multi-Class Imbalance Learning

Cruz

Souza

Sabourin

et al. 2019

Int. J. Patt. Recogn. Artif. Intell.

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Class-imbalance refers to classification problems in which many more instances are available for certain classes than for others. Such imbalanced datasets require special attention because traditional classifiers generally favor the majority class which has a large number of instances. Ensemble of classifiers have been reported to yield promising results. However, the majority of ensemble methods applied too imbalanced learning are static ones. Moreover, they only deal with binary imbalanced problems. Hence, this paper presents an empirical analysis of dynamic selection techniques and data preprocessing methods for dealing with multi-class imbalanced problems. We considered five variations of preprocessing methods and four dynamic selection methods. Our experiments conducted on 26 multiclass imbalanced problems show that the dynamic ensemble improves the F-measure and the G-mean as compared to the static ensemble. Moreover, data preprocessing plays an important role in such cases.

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A study on combining dynamic selection and data preprocessing for imbalance learning

Cited by 105 publications

References 47 publications

FIRE-DES++: Enhanced online pruning of base classifiers for dynamic ensemble selection

FIRE-DES++: Enhanced online pruning of base classifiers for dynamic ensemble selection

Evaluation of Sampling-Based Ensembles of Classifiers on Imbalanced Data for Software Defect Prediction Problems

Dynamic Ensemble Selection and Data Preprocessing for Multi-Class Imbalance Learning

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