A new adaptive weighted imbalanced data classifier via improved support vector machines with high-dimension nature

Qi, Kai; Yang, Hu; Hu, Qingyu; Yang, Dezhi

doi:10.1016/j.knosys.2019.104933

Cited by 23 publications

(9 citation statements)

References 22 publications

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“…They also require an in-depth understanding of how a given training procedure is conducted and what specific part of it may lead to bias toward the majority class. The most commonly addressed issues with the algorithmic approach are developing novel skew-insensitive split criteria for decision trees [27]- [29], using instance weighting for support vector machines [30]- [32], or modifying the way different layers are trained in deep learning [33]- [35]. Furthermore, cost-sensitive solutions [36]- [38] and one-class classification [39]- [41] can also be considered as a form of algorithm-level approaches.…”

Section: B Algorithm-level Approachesmentioning

confidence: 99%

DeepSMOTE: Fusing Deep Learning and SMOTE for Imbalanced Data

Dablain

Krawczyk

Chawla

2023

IEEE Trans. Neural Netw. Learning Syst.

215

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Despite over two decades of progress, imbalanced data is still considered a significant challenge for contemporary machine learning models. Modern advances in deep learning have further magnified the importance of the imbalanced data problem, especially when learning from images. Therefore, there is a need for an oversampling method that is specifically tailored to deep learning models, can work on raw images while preserving their properties, and is capable of generating highquality, artificial images that can enhance minority classes and balance the training set. We propose Deep synthetic minority oversampling technique (SMOTE), a novel oversampling algorithm for deep learning models that leverages the properties of the successful SMOTE algorithm. It is simple, yet effective in its design. It consists of three major components: 1) an encoder/decoder framework; 2) SMOTE-based oversampling; and 3) a dedicated loss function that is enhanced with a penalty term. An important advantage of DeepSMOTE over generative adversarial network (GAN)-based oversampling is that DeepSMOTE does not require a discriminator, and it generates high-quality artificial images that are both information-rich and suitable for visual inspection. DeepSMOTE code is publicly available at https://github.com/dd1github/DeepSMOTE.

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Section: B Algorithm-level Approachesmentioning

confidence: 99%

DeepSMOTE: Fusing Deep Learning and SMOTE for Imbalanced Data

Dablain

Krawczyk

Chawla

2023

IEEE Trans. Neural Netw. Learning Syst.

215

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show abstract

“…They also require an in-depth understanding of how a given training procedure is conducted and what specific part of it may lead to bias towards the majority class. The most commonly addressed issues with the algorithmic approach are developing novel skew-insensitive split criteria for decision trees [22], [23], [24], using instance weighting for Support Vector Machines [25], [26], [27], or modifying the way different layers are trained in deep learning [28], [29], [30]. Furthermore, costsensitive solutions [31], [32], [33] and one-class classification [34], [35], [36] can also be considered as a form of algorithmlevel approaches.…”

Section: Learning From Imbalanced Datamentioning

confidence: 99%

DeepSMOTE: Fusing Deep Learning and SMOTE for Imbalanced Data

Dablain¹,

Krawczyk²,

Chawla³

2021

Preprint

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Despite over two decades of progress, imbalanced data is still considered a significant challenge for contemporary machine learning models. Modern advances in deep learning have magnified the importance of the imbalanced data problem. The two main approaches to address this issue are based on loss function modifications and instance resampling. Instance sampling is typically based on Generative Adversarial Networks (GANs), which may suffer from mode collapse. Therefore, there is a need for an oversampling method that is specifically tailored to deep learning models, can work on raw images while preserving their properties, and is capable of generating high quality, artificial images that can enhance minority classes and balance the training set. We propose DeepSMOTE -a novel oversampling algorithm for deep learning models. It is simple, yet effective in its design. It consists of three major components: (i) an encoder/decoder framework; (ii) SMOTEbased oversampling; and (iii) a dedicated loss function that is enhanced with a penalty term. An important advantage of DeepSMOTE over GAN-based oversampling is that DeepSMOTE does not require a discriminator, and it generates high-quality artificial images that are both information-rich and suitable for visual inspection. DeepSMOTE code is publicly available at: https://github.com/dd1github/DeepSMOTE.

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“…One of the best and most powerful support vector finder method was proposed by Cortes andVapnik in 1995 (Cortes &Vapnik, 1995) under the title of "support vector network"; this has become a powerful tool in many fields, such as classification, pattern recognition, detection, gene selection, and so on (Qi, Yang, Hu, & Yang, 2019). In time, many research studies have been carried out based on it; also, efforts have been made to develop variants of it.…”

Section: Literature Reviewmentioning

confidence: 99%

An evolutionary Pentagon Support Vector finder method

Mousavi

Charles

Gherman

2020

Expert Systems with Applications

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In dealing with big data, we need effective algorithms; effectiveness that depends, among others, on the ability to remove outliers from the data set, especially when dealing with classification problems. To this aim, support vector finder algorithms have been created to save just the most important data in the data pool. Nevertheless, existing classification algorithms, such as Fuzzy C-Means (FCM), suffer from the drawback of setting the initial cluster centers imprecisely. In this paper, we avoid existing shortcomings and aim to find and remove unnecessary data in order to speed up the final classification task without losing vital samples and without harming final accuracy; in this sense, we present a unique approach for finding support vectors, named evolutionary pentagon support vector (PSV) finder method. The originality of the current research lies in using geometrical computations and evolutionary algorithms to make a more effective system, which has the advantage of higher accuracy in some data sets. The proposed method is subsequently tested with seven benchmark data sets and the results are compared to those obtained from performing classification on the original data (classification before and after PSV) under the same conditions. The testing returned promising results.

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A new adaptive weighted imbalanced data classifier via improved support vector machines with high-dimension nature

Cited by 23 publications

References 22 publications

DeepSMOTE: Fusing Deep Learning and SMOTE for Imbalanced Data

DeepSMOTE: Fusing Deep Learning and SMOTE for Imbalanced Data

DeepSMOTE: Fusing Deep Learning and SMOTE for Imbalanced Data

An evolutionary Pentagon Support Vector finder method

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