Multi-Imbalance: An open-source software for multi-class imbalance learning

Zhang, Chongsheng; Bi, Jingjun; Xu, Shixin; Ramentol, Enislay; Fan, Gaojuan; Qiao, Baojun; Fujita, Hamido

doi:10.1016/j.knosys.2019.03.001

Cited by 140 publications

(32 citation statements)

References 25 publications

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“…75 KEEL incorporates 45 algorithms for imbalanced classification that include cost-sensitive learning, ensemble, and resampling. Multi-imbalance 60 is an open-source software for multi-class imbalanced datasets. This toolbox includes 18 algorithms for multi-class learning in an imbalanced scenario.…”

Section: Software Toolsmentioning

confidence: 99%

“…Another important open research problem is that of multi-class imbalance. 60 Most of the solutions offered so far are for binary class datasets where the majority vs minority class boundary is neatly defined. 85 Experiments in Reference 14 proved that, in most cases, these solutions were ineffective for handing imbalance in multi-class datasets.…”

Section: Handling Multi-class Imbalancementioning

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: Software Toolsmentioning

confidence: 99%

Section: Handling Multi-class Imbalancementioning

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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“…A new Decision Tree (DT) ensemble was proposed [63] to increase the diversity of the ensemble by using different training sample numbers for different base DT classifiers. Another approach for multi-class and imbalanced data was presented [64] in which the binary classifiers are first created and then integrated in the ensemble by using majority voting to make predictions.…”

Section: Related Workmentioning

confidence: 99%

A heterogeneous online learning ensemble for non-stationary environments

Idrees

Minku

Stahl

et al. 2020

Knowledge-Based Systems

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Learning in non-stationary environments is a challenging task which requires the updating of predictive models to deal with changes in the underlying probability distribution of the problem, i.e., dealing with concept drift. Most work in this area is concerned with updating the learning system so that it can quickly recover from concept drift, while little work has been dedicated to investigating what type of predictive model is most suitable at any given time. This paper aims to investigate the benefits of online model selection for predictive modelling in nonstationary environments. A novel heterogeneous ensemble approach is proposed to intelligently switch between different types of base models in an ensemble to increase the predictive performance of online learning in nonstationary environments. This approach is Heterogeneous Dynamic Weighted Majority (HDWM). It makes use of "seed" learners of different types to maintain ensemble diversity, overcoming problems of existing dynamic ensembles that may undergo loss of diversity due to the exclusion of base learners. The algorithm has been evaluated on artificial and real-world data streams against existing well-known approaches such as a heterogeneous Weighted Majority Algorithm (WMA) and a homogeneous Dynamic Weighted Majority (DWM). The results show that HDWM performed significantly better than WMA in non-stationary environments. Also, when recurring concept drifts were present, the predictive performance of HDWM showed an improvement over DWM.

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“…In the field of deep learning, researchers are more interested in exploring the network structures to improve its performance and there are few work on the research of class imbalance. However, in real datasets for training, class imbalance is widespread [24], [25]. With the extensive application of deep learning to various practical fields, the class imbalance problems will be paid more and more attention.…”

Section: Related Workmentioning

confidence: 99%

Constrained-Focal-Loss Based Deep Learning for Segmentation of Spores

et al. 2019

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The statistics of disease spores is significant for early strategy design of disease control in precision agriculture. To obtain the statistics information of spores in microscopic images, it is crucial to segment spores from images. In this paper, we research a deep learning based method to segment spores, taking anthrax spores as the research objects. We first built an anthrax spore dataset consisting of more than 40,000 spores with accurate labeled spore boundaries to advance the state of the art technology of spore statistics. Then on consideration of the complex class imbalances in actual anthrax spore images, we investigate how class imbalances and hard examples simultaneously influence the loss during training and we discover that hard examples are more likely to appear at the pixels of rare pixels, such as small class pixels and contour pixels. Based on this discovery, we propose Constrained Focal Loss (CFL), which focuses on small class objects, and has a constrained term related to hard examples. In addition, we further propose CFL * , where high importance is put on the pixels surrounding spore contours to improve classification accuracy. The results show that the mean IoU of the DeepLabv3+ trained with CFL * (called as CFL * Net) achieves 91.0%, higher than original DeepLabv3+ with cross-entropy by 8.6 points, and the DeepLabv3+ with Focal Loss by 10.4 points. Moreover, CFLNet * can achieve better performance than original DeepLabv3+, using less than one-third of the training samples and half of the training steps. INDEX TERMS Image segmentation, class imbalance, focal Loss, hard example, convolutional neural networks (CNN).

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Multi-Imbalance: An open-source software for multi-class imbalance learning

Cited by 140 publications

References 25 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

A heterogeneous online learning ensemble for non-stationary environments

Constrained-Focal-Loss Based Deep Learning for Segmentation of Spores

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