2021
DOI: 10.48550/arxiv.2105.01198
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Weighted Least Squares Twin Support Vector Machine with Fuzzy Rough Set Theory for Imbalanced Data Classification

Maysam Behmanesh,
Peyman Adibi,
Hossein Karshenas

Abstract: Support vector machines (SVMs) are powerful supervised learning tools developed to solve classification problems. However, SVMs are likely to perform poorly in the classification of imbalanced data. The rough set theory presents a mathematical tool for inference in nondeterministic cases that provides methods for removing irrelevant information from data. In this work, we propose an approach that efficiently used fuzzy rough set theory in weighted least squares twin support vector machine called FRLSTSVM for c… Show more

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“…It had seven different categories of a multiclass imbalance learning algorithm. Behmanesh et al [30] proposed an approach that used fuzzy rough set theory in weighted least square twin support vector machine (FRLSTSVM) to classify imbalanced data. To create a hyperplane, the data points from the minority class remain unchanged.…”
Section: Literature Surveymentioning
confidence: 99%
“…It had seven different categories of a multiclass imbalance learning algorithm. Behmanesh et al [30] proposed an approach that used fuzzy rough set theory in weighted least square twin support vector machine (FRLSTSVM) to classify imbalanced data. To create a hyperplane, the data points from the minority class remain unchanged.…”
Section: Literature Surveymentioning
confidence: 99%