Classification study for the imbalanced data based on Biased-SVM and the modified over-sampling algorithm

Zhang, Liumei; Tan, Baoyu; Liu, Tianshi; Sun, Xiaoqun

doi:10.1088/1742-6596/1237/2/022052

Cited by 7 publications

(3 citation statements)

References 8 publications

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“…In the classification of support vector machines, support vector plays a decisive role in the classification of hyperplanes. Because in random SMOTE algorithm, sampling of all the minority class samples will lead to a large amount of redundancy, which will further increase the training time, reduce the quality of the training sample, therefore, Zhang et al [19] combine their improved random SMOTE oversampling algorithm and Bias-SVM classification method together, and only consider generating the sample that is "close" to the boundary， proposing an improved unbalanced data sets classification method. This algorithm processes unbalanced data from the data level and algorithm level, and can cluster minority class samples, and determine the support vector as the parent sample according to the distance between minority class clustering center and majority class clustering center.…”

Section: Unbalanced Data Sets Classification Methods Based On Sampmentioning

confidence: 99%

Review of Classification Methods on Unbalanced Data Sets

et al. 2021

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This paper studies the classification of unbalanced data sets. First, this kind of data sets is briefly introduced, and then the classification methods of unbalanced data sets are analyzed in detail from different perspectives such as data sampling method, algorithm level, feature level, cost-sensitive function, and deep learning. In addition, the data sampling methods are divided into different technologies for introduction: unbalanced data set classification method based on synthetic minority over-sampling technology (SMOTE), support vector machine (SVM) technology, and k-nearest neighbor (KNN) technology, etc. Then, the advantages and disadvantages of these methods are compared. Finally, the evaluation criteria of the unbalanced data set classifier are summarized, and the future work directions are prospected and summarized.

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Section: Unbalanced Data Sets Classification Methods Based On Sampmentioning

confidence: 99%

Review of Classification Methods on Unbalanced Data Sets

et al. 2021

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“…Although biased SVM is originally proposed for positive unlabeled learning, its promising performance in imbalance environment has been verified by many studies [46][47][48]. When compared with traditional SVM classifier biased towards majority class, biased SVM is able to achieve good performance especially for minority class by assigning proper values to C 1 and C 2 .…”

Section: Impact Of Imbalance Environmentmentioning

confidence: 99%

Effectively predicting HIV-1 protease cleavage sites by using an ensemble learning approach

Tang

et al. 2022

BMC Bioinformatics

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Background The site information of substrates that can be cleaved by human immunodeficiency virus 1 proteases (HIV-1 PRs) is of great significance for designing effective inhibitors against HIV-1 viruses. A variety of machine learning-based algorithms have been developed to predict HIV-1 PR cleavage sites by extracting relevant features from substrate sequences. However, only relying on the sequence information is not sufficient to ensure a promising performance due to the uncertainty in the way of separating the datasets used for training and testing. Moreover, the existence of noisy data, i.e., false positive and false negative cleavage sites, could negatively influence the accuracy performance. Results In this work, an ensemble learning algorithm for predicting HIV-1 PR cleavage sites, namely EM-HIV, is proposed by training a set of weak learners, i.e., biased support vector machine classifiers, with the asymmetric bagging strategy. By doing so, the impact of data imbalance and noisy data can thus be alleviated. Besides, in order to make full use of substrate sequences, the features used by EM-HIV are collected from three different coding schemes, including amino acid identities, chemical properties and variable-length coevolutionary patterns, for the purpose of constructing more relevant feature vectors of octamers. Experiment results on three independent benchmark datasets demonstrate that EM-HIV outperforms state-of-the-art prediction algorithm in terms of several evaluation metrics. Hence, EM-HIV can be regarded as a useful tool to accurately predict HIV-1 PR cleavage sites.

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“…The traditional classifiers such as logistic regression, SVM, ANN, and decision tree are appropriate for balanced training sets (Zhang et al 2019). These methods often derive suboptimal classification results, i.e.…”

Section: Machine Learning Algorithmsmentioning

confidence: 99%

Bankruptcy Prediction Using Deep Learning Approach Based on Borderline SMOTE

Smiti

Soui

2020

Inf Syst Front

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Imbalanced classification on bankruptcy prediction is considered as one of the most important topics in financial institutions. In this context, various statistical and artificial intelligence methods have been proposed. Recently, deep learning algorithms are experiencing a resurgence of interest, and are widely used to build a prediction and classification models. To this end, we propose a novel deep learning-based approach called BSM-SAES. This approach combines Borderline Synthetic Minority oversampling technique (BSM) and Stacked AutoEncoder (SAE) based on the Softmax classifier. The aim is to develop an accurate and reliable bankruptcy prediction model which includes the features extraction process. To assess the classification performance of our proposed model, k-nearest neighbor, decision tree, support vector machine, and artificial neural network, C5.0 that are machine learning methods, are applied. We evaluate our proposed approach on the Polish imbalanced datasets. The obtained results confirm the efficiency of our proposed model compared to other machine learning models regarding predicting and classifying the financial status of a firm.

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Classification study for the imbalanced data based on Biased-SVM and the modified over-sampling algorithm

Cited by 7 publications

References 8 publications

Review of Classification Methods on Unbalanced Data Sets

Review of Classification Methods on Unbalanced Data Sets

Effectively predicting HIV-1 protease cleavage sites by using an ensemble learning approach

Bankruptcy Prediction Using Deep Learning Approach Based on Borderline SMOTE

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