Entropy based fuzzy least squares twin support vector machine for class imbalance learning

Gupta, Deepak; Richhariya, Bharat

doi:10.1007/s10489-018-1204-4

Cited by 45 publications

(14 citation statements)

References 30 publications

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“…We acknowledge an existence of a wide range of diverse econometric techniques applicable for studying coherence and contagion patterns, namely, VaR ( [69], among many others), entropy-based fuzzy least squares twin support vector machine approach [12], several variance decomposition and time-varying connectedness approaches ( [70], and the references therein), unconstrained convex minimization based implicit Lagrangian twin extreme learning machine technique [71], density-weighted support vector machines approach [72], among many other techniques, we stay with the wavelet-based approach because of the rationale discussed below.…”

Section: Time-frequency Wavelet Analysismentioning

confidence: 99%

“…Many developing and developed states have drastically restricted people´s mobility aiming to contain further advancement in the virus propagation. Global economy and financial markets have been profoundly impacted by this Covid-19 fueled crisis ( [5][6][7][8][9][10][11][12]; among others). The pandemic crisis has brought about unforeseen volatility in government bond yields.…”

Section: Introductionmentioning

confidence: 99%

“…In this global climate of high uncertainty, it is desirable to reassess the diversification potential and eventual safe haven attribute of the sovereign debt, issued by leading emerging market (EM) countries. Being inspired by [12], dealing with the UST securities´yield-curve analyses and hedge properties, in this paper we study the shape of the yield curves of the five major emerging EM e conomies, namely, as Federative Republic of Brazil, Russian Federation, Republic of India, People's Republic of China, and the Republic of South Africa, being jointly known as BRICS.…”

Section: Introductionmentioning

confidence: 99%

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The impact of the Covid-19 related media coverage upon the five major developing markets

2021

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This paper analyses the influence of the Covid-19 coverage by the social media upon the shape of the sovereign yield curves of the five major developing countries, namely Federative Republic of B razil, Russian Federation, Republic of India, People’s Republic of China, and the Republic of South Africa (BRICS). The coherenc e between the level, slope, and the curvature of the sovereign yield term structures and the Covid-19 medi a coverage is found to vary between low and high ranges, depending on the phases of the pandemic. The empirical estimations of the yield-curve factors a re performed by means of the Diebold–Li modified version of the Nelson–Siegel model. The intervals of low coherence reveal the capacity of the two latent factors, level and slope, to be used for creating cross-factor diversification strategies, workable under crisis conditions, as evidenced on the example of the ongoing pandemic. Diverse coherence patterns are reported on a per-country basis, highlighting a promising potential of sovereign debt investments for designing cross-country and cross-factor fixed-income strategies, capable of hedging downside risks.

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Section: Time-frequency Wavelet Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The impact of the Covid-19 related media coverage upon the five major developing markets

2021

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“…In resolving the problem of class imbalance, Gupta and Richhariya in [25] presented entropy-based fuzzy least squares support vector machine and entropy-based fuzzy least squares twin support vector machine. Fuzzy membership was calculated on entropy values of samples.…”

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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“…Following EFSVM, Gupta et al [54] proposed a fuzzy twin support vector machine based on information entropy which is termed as EFTWSVM-CIL. At the same time, Gupta et al [55] proposed a new fuzzy least squares twin support vector machine (EFLSTWSVM-CIL) for class imbalance learning. Moreover, Richhariya and Tanveer [56] proposed a robust fuzzy least squares twin SVM (RFLSTSVM-CIL) for class imbalance learning.…”

Section: Introductionmentioning

confidence: 99%

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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Entropy based fuzzy least squares twin support vector machine for class imbalance learning

Cited by 45 publications

References 30 publications

The impact of the Covid-19 related media coverage upon the five major developing markets

The impact of the Covid-19 related media coverage upon the five major developing markets

Solving Misclassification of the Credit Card Imbalance Problem Using Near Miss

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

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