A fuzzy based Lagrangian twin parametric-margin support vector machine (FLTPMSVM)

Gupta, Deepak; Borah, Parashjyoti; Prasad, Mukesh

doi:10.1109/ssci.2017.8280964

Cited by 17 publications

(4 citation statements)

References 20 publications

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“…Fuzzy membership was calculated on entropy values of samples. In another study by Gupta et al in [26], a new method was referred to as fuzzy Lagrangian twin parametric-margin support vector machine which used fuzzy membership values in decision learning to handle outlier points. Hazarika and Gupta in [27] used a support vector machine based on density weight to handle the imbalance of classes problem.…”

Section: Related Workmentioning

confidence: 99%

Solving Misclassification of the Credit Card Imbalance Problem Using Near Miss

Mqadi

Naicker

Adeliyi

2021

Mathematical Problems in Engineering

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In ordinary credit card datasets, there are far fewer fraudulent transactions than ordinary transactions. In dealing with the credit card imbalance problem, the ideal solution must have low bias and low variance. The paper aims to provide an in-depth experimental investigation of the effect of using a hybrid data-point approach to resolve the class misclassification problem in imbalanced credit card datasets. The goal of the research was to use a novel technique to manage unbalanced datasets to improve the effectiveness of machine learning algorithms in detecting fraud or anomalous patterns in huge volumes of financial transaction records where the class distribution was imbalanced. The paper proposed using random forest and a hybrid data-point approach combining feature selection with Near Miss-based undersampling technique. We assessed the proposed method on two imbalanced credit card datasets, namely, the European Credit Card dataset and the UCI Credit Card dataset. The experimental results were reported using performance matrices. We compared the classification results of logistic regression, support vector machine, decision tree, and random forest before and after using our approach. The findings showed that the proposed approach improved the predictive accuracy of the logistic regression, support vector machine, decision tree, and random forest algorithms in credit card datasets. Furthermore, we found that, out of the four algorithms, the random forest produced the best results.

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Section: Related Workmentioning

confidence: 99%

Solving Misclassification of the Credit Card Imbalance Problem Using Near Miss

Mqadi

Naicker

Adeliyi

2021

Mathematical Problems in Engineering

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“…Then, putting (52) into (43) and using (44)-(46), we can get the Wolfe dual problem of (41) as follows.…”

Section: B Linear Eftbsvmmentioning

confidence: 99%

“…Step 3. Choose the proper penalty parameters c i (i = 1, 2, 3, 4) and then obtain the solutions (52) and 54, respectively.…”

Section: Algorithm 1 Linear Eftbsvm Classifiermentioning

confidence: 99%

“…In order to overcome this problem in FSVM, a new fuzzy SVM for imbalance datasets was proposed to reduce the misclassification accuracy of the minority class in FSVM [46]. In addition, based on utilizing fuzzy membership, various Fuzzy SVMs were presented such as NFSVM [47], Bilateral-weighted FSVM (BFSVM) [48], WCS-FSVM [49], FTSVM [50], NFTSVM [51] and FLTPMSVM [52], etc.…”

Section: Introductionmentioning

confidence: 99%

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Entropy-Based Fuzzy Twin Bounded Support Vector Machine for Binary Classification

et al. 2019

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Twin support vector machine (TWSVM) is a new machine learning method, as opposed to solving a single quadratic programming problem in support vector machine (SVM), which generates two nonparallel hyperplanes by solving two smaller size quadratic programming problems. However, the TWSVM obtains the final classifier by giving the same importance to all training samples which may be important for classification performance. In order to address this problem, in this paper, we propose a novel entropy-based fuzzy twin bounded support vector machine (EFTBSVM) for binary classification problems. By considering the fuzzy membership value for each sample and assigning it based on the entropy value, the samples with higher class certainty are assigned to relatively larger fuzzy membership. In addition, the proposed EFTBSVM not only maintains the superior characteristics of the TWSVM but also exploits the structural risk minimization principle by introducing a regularization term. The experimental results achieved on synthetic datasets and benchmark datasets illustrate the effectiveness of the proposed method.

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