  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 classification of
imbalanced data. The first innovation is introducing a new fuzzy rough
set-based under-sampling strategy to make the classifier robust in terms of the
imbalanced data. For constructing the two proximal hyperplanes in FRLSTSVM,
data points from the minority class remain unchanged while a subset of data
points in the majority class are selected using a new method. In this model, we
embed the weight biases in the LSTSVM formulations to overcome the bias
phenomenon in the original twin SVM for the classification of imbalanced data.
In order to determine these weights in this formulation, we introduce a new
strategy that uses fuzzy rough set theory as the second innovation.
Experimental results on the famous imbalanced datasets, compared to the related
traditional SVM-based methods, demonstrate the superiority of the proposed
FRLSTSVM model in the imbalanced data classification.
