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Support vector machines (SVMs) are powerful supervised learning toolsdeveloped to solve classification problems. However, SVMs are likely to performpoorly in the classification of imbalanced data. The rough set theory presentsa mathematical tool for inference in nondeterministic cases that providesmethods for removing irrelevant information from data. In this work, we proposean approach that efficiently used fuzzy rough set theory in weighted leastsquares twin support vector machine called FRLSTSVM for classification ofimbalanced data. The first innovation is introducing a new fuzzy roughset-based under-sampling strategy to make the classifier robust in terms of theimbalanced data. For constructing the two proximal hyperplanes in FRLSTSVM,data points from the minority class remain unchanged while a subset of datapoints in the majority class are selected using a new method. In this model, weembed the weight biases in the LSTSVM formulations to overcome the biasphenomenon in the original twin SVM for the classification of imbalanced data.In order to determine these weights in this formulation, we introduce a newstrategy that uses fuzzy rough set theory as the second innovation.Experimental results on the famous imbalanced datasets, compared to the relatedtraditional SVM-based methods, demonstrate the superiority of the proposedFRLSTSVM model in the imbalanced data classification.

Weighted Least Squares Twin Support Vector Machine with Fuzzy Rough Set Theory for Imbalanced Data Classification