Entropy-Based Fuzzy Least Squares Twin Support Vector Machine for Pattern Classification
Least squares twin support vector machine (LSTSVM) is a new machine learning method, as opposed to solving two quadratic programming problems in twin support vector machine (TWSVM), which generates two nonparallel hyperplanes by solving a pair of linear system of equations. However, LSTSVM obtains the resultant classifier by giving same importance to all training samples which may be important for classification performance. In this paper, by considering the fuzzy membership value for each sample, we propose an entropy-based fuzzy least squares twin support vector machine where fuzzy membership values are assigned based on the entropy values of all training samples. The proposed method not only retains the superior characteristics of LSTSVM which is simple and fast algorithm, but also implements the structural risk minimization principle to overcome the possible over- fitting problem. Experiments are performed on several synthetic as well as benchmark datasets and the experimental results illustrate the effectiveness of our method.
KeywordsPattern classification Information entropy Least squares twin support vector machine Fuzzy membership
This work was partially supported by the National Natural Science Foundation of China (Grant No. 61702012), the University Outstanding Young Talent Support Project of Anhui Province of China (Grant No. gxyq2017026), the University Natural Science Research Project of Anhui Province of China (Grant Nos. KJ2016A431, KJ2017A361 and KJ2017A368) and the Program for Innovative Research Team in Anqing Normal University.
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Conflict of interest
The authors declare that there is no conflict of interests regarding the publication of this paper.
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