Evolutionary inversion of class distribution in overlapping areas for multi-class imbalanced learning

Fernandes, Everlandio; Carvalho, André C. P. L. F. de

doi:10.1016/j.ins.2019.04.052

Cited by 42 publications

(15 citation statements)

References 29 publications

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“…A recent work that addresses multi-class imbalance is Evolutionary inversion of class-distribution in overlapping class regions proposed by Fernandes and de Carvalho. 70 This method focuses only on the minority instances lying in the overlapping regions between classes since these have more class-discriminatory information. A multi-objective evolutionary algorithm is used to determine the population for each individual classifier in an ensemble of classifiers with a greater selection of minority instances being from the overlapping region.…”

Section: Hybrid Sampling Strategiesmentioning

confidence: 99%

“…The resulting entropy‐based hybrid sampling encourages a new research pattern that involves statistical machine learning for finding discriminatory samples. A recent work that addresses multi‐class imbalance is Evolutionary inversion of class‐distribution in overlapping class regions proposed by Fernandes and de Carvalho 70 . This method focuses only on the minority instances lying in the overlapping regions between classes since these have more class‐discriminatory information.…”

Section: Hybrid Sampling Strategiesmentioning

confidence: 99%

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The balancing trick: Optimized sampling of imbalanced datasets—A brief survey of the recent State of the Art

Susan

Kumar

2020

Engineering Reports

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This survey paper focuses on one of the current primary issues challenging data mining researchers experimenting on real-world datasets. The problem is that of imbalanced class distribution that generates a bias toward the majority class due to insufficient training samples from the minority class. The current machine learning and deep learning algorithms are trained on datasets that are insufficiently represented in certain categories. On the other hand, some other classes have surplus samples due to the ready availability of data from these categories.Conventional solutions suggest undersampling of the majority class and/or oversampling of the minority class for balancing the class distribution prior to the learning phase. Though this problem of uneven class distribution is, by and large, ignored by researchers focusing on the learning technology, a need has now arisen for incorporating balance correction and data pruning procedures within the learning process itself. This paper surveys a plethora of conventional and recent techniques that address this issue through intelligent representations of samples from the majority and minority classes, that are given as input to the learning module. The application of nature-inspired evolutionary algorithms to intelligent sampling is examined, and so are hybrid sampling strategies that select and retain the difficult-to-learn samples and discard the easy-to-learn samples. The findings by various researchers are summarized to a logical end, and various possibilities and challenges for future directions in research are outlined. K E Y W O R D Sclass-imbalance problem, hybrid sampling, imbalanced data, oversampling, sampling, undersampling INTRODUCTIONLearning from imbalanced datasets results in a bias toward the majority class whose labeled samples are available in plenty as compared to the insufficiently represented minority class. 1 In data mining, factors that bring down the classifier performance are the intrinsic characteristics of the data and an uneven class distribution. 2 Lack of adequate data in the minority class results in a fuzzy and ever-varying decision boundary, leading to erroneous results. The problem isThis is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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Section: Hybrid Sampling Strategiesmentioning

confidence: 99%

Section: Hybrid Sampling Strategiesmentioning

confidence: 99%

The balancing trick: Optimized sampling of imbalanced datasets—A brief survey of the recent State of the Art

Susan

Kumar

2020

Engineering Reports

View full text Add to dashboard Cite

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“…23 Another ensemble-based method utilizing an Evolutionary Algorithm (EA) was proposed. 24 EA was used to selectively remove overlapped majority class instances. These ensemble-based methods often showed better performance than other state-of-the-art methods.…”

Section: Related Workmentioning

confidence: 99%

Improved Overlap-based Undersampling for Imbalanced Dataset Classification with Application to Epilepsy and Parkinson’s Disease

Vuttipittayamongkol

Elyan

2020

Int. J. Neur. Syst.

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Classification of imbalanced datasets has attracted substantial research interest over the past decades. Imbalanced datasets are common in several domains such as health, finance, security and others. A wide range of solutions to handle imbalanced datasets focus mainly on the class distribution problem and aim at providing more balanced datasets by means of resampling. However, existing literature shows that class overlap has a higher negative impact on the learning process than class distribution. In this paper, we propose overlap-based undersampling methods for maximizing the visibility of the minority class instances in the overlapping region. This is achieved by the use of soft clustering and the elimination threshold that is adaptable to the overlap degree to identify and eliminate negative instances in the overlapping region. For more accurate clustering and detection of overlapped negative instances, the presence of the minority class at the borderline areas is emphasized by means of oversampling. Extensive experiments using simulated and real-world datasets covering a wide range of imbalance and overlap scenarios including extreme cases were carried out. Results show significant improvement in sensitivity and competitive performance with well-established and state-of-the-art methods.

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“…Overlapping conditions can increase the accuracy of one class by decreasing the accuracy of another class. For example, although the overlapping regions have a high concentration of minority classes, the classification results can also provide low accuracy because some instances associated with majority classes are eliminated [6].…”

Section: Introductionmentioning

confidence: 99%

“…However, on the other hand with the noise, the performance given by feature selection can decrease [9] and noise basically has an influence on classification performance [10]. Noise handling in general uses the method of resampling, but often encounters obstacles if there is a state of overlapping [6]. This is an obstacle when handling multi-class imbalance and at the same time also faces other obstacles in the form of overlapping and noise.…”

Section: Introductionmentioning

confidence: 99%

Hybrid approach redefinition-multi class with resampling and feature selection for multi-class imbalance with overlapping and noise

Ongko

Hartono

2021

Bulletin EEI

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Class imbalance and overlapping on multi-class can reduce the performance and accuracy of the classification. Noise must also be considered because it can reduce the performance of classification. With a resampling algorithm and feature selection, this paper proposes a method for improving the performance of hybrid approach redefinition-multi class (HAR-MI). Resampling algorithm can overcome the problem of noise but cannot handle overlapping well. Feature selection is good at dealing with overlapping but can experience a decrease in quality if there is a noise. The HAR-MI approach is a way to deal with multi-class imbalance issues, but it has some drawbacks when dealing with overlapping. The contribution of this paper is to suggest a new approach for dealing with class imbalance, overlapping, and noise in multi-class. This is accomplished by employing minimizing overlapping selection (MOSS) as an ensemble learning algorithm and a preprocessing technique in HAR-MI, as well as employing multi-class combination cleaning and resampling (MC-CCR) as a resampling algorithm at the processing stage. When subjected to overlapping and classifier performance, it is discovered that the proposed method produces good results, as evidenced by higher augmented r-value, class average accuracy, class balance accuracy, multi class g-mean, and confusion entropy.

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Evolutionary inversion of class distribution in overlapping areas for multi-class imbalanced learning

Cited by 42 publications

References 29 publications

The balancing trick: Optimized sampling of imbalanced datasets—A brief survey of the recent State of the Art

The balancing trick: Optimized sampling of imbalanced datasets—A brief survey of the recent State of the Art

Improved Overlap-based Undersampling for Imbalanced Dataset Classification with Application to Epilepsy and Parkinson’s Disease

Hybrid approach redefinition-multi class with resampling and feature selection for multi-class imbalance with overlapping and noise

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