2019
DOI: 10.1007/s10044-019-00851-x
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Instance-based entropy fuzzy support vector machine for imbalanced data

Abstract: Imbalanced classification has been a major challenge for machine learning because many standard classifiers mainly focus on balanced datasets and tend to have biased results towards the majority class. We modify entropy fuzzy support vector machine (EFSVM) and introduce instance-based entropy fuzzy support vector machine (IEFSVM). Both EFSVM and IEFSVM use the entropy information of k-nearest neighbors to determine the fuzzy membership value for each sample which prioritizes the importance of each sample. IEFS… Show more

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Cited by 9 publications
(8 citation statements)
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“…As previously mentioned, we employ IEFSVM in [26], an advanced model of EFSVM, for loan prediction in P2P lending market. Many literatures have proposed methods to compute the fuzzy membership of FSVM; Fan et al [12] suggested to utilize the EFSVM by incorporating the nearest neighbors entropy for imbalanced dataset.…”
Section: A Entropy Fuzzy Support Vector Machine (Efsvm)mentioning
confidence: 99%
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“…As previously mentioned, we employ IEFSVM in [26], an advanced model of EFSVM, for loan prediction in P2P lending market. Many literatures have proposed methods to compute the fuzzy membership of FSVM; Fan et al [12] suggested to utilize the EFSVM by incorporating the nearest neighbors entropy for imbalanced dataset.…”
Section: A Entropy Fuzzy Support Vector Machine (Efsvm)mentioning
confidence: 99%
“…However, the disadvantage of this method lies on a unified k for all instances. In this paper, we suggest to utilize IEFSVM proposed in [26] to reflect the change of entropy, which varies in response to different k, in determination of fuzzy membership. The determination of such fuzzy membership is based on the following diversity pattern of nearest neighbors entropy.…”
Section: B Instance-based Entropy Fuzzy Support Vector Machine (Iefsvm)mentioning
confidence: 99%
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“…Gupta et al [10] further proposed a fuzzy twin SVM based on information entropy. Cho et al [6] modified EFSVM and introduced instance-based entropy fuzzy SVM. By combining entropy information of Universum data, a fuzzy Universum SVM (FUSVM) and a fuzzy Universum twin SVM are proposed in [23].…”
mentioning
confidence: 99%