Weighted support vector machine using fuzzy rough set theory

The existence of both uncertainty and imprecision has detrimental impact on efficiency of decision-making applications and some machine learning methods, in particular support vector machine in which noisy samples diminish the performance of SVM training. Therefore, it is important to introduce a special method in order to improve this problem. Fuzzy aspects can handle mentioned problem which has been considered in some classification methods. This paper presents a novel weighted support vector machine to improve the noisy sensitivity problem of standard support vector machine for multiclass data classification. The basic idea is considered to add a weighted coefficient to the penalty term Lagrangian formula for optimization problem, which is called entropy degree, using lower and upper approximation for membership function in fuzzy rough set theory. As a result, noisy samples have low degree and important samples have high degree. To evaluate the power of the proposed method WSVM-FRS (Weighted SVM-Fuzzy Rough Set), several experiments have been conducted based on tenfold cross-validation over real-world data sets from UCI repository and MNIST data set. Experimental results show that the proposed method is superior than the other state-of-the-art competing methods regarding accuracy, precision and recall metrics.

Automated summary

This study proposed a novel weighted support vector machine to address the noisy sensitivity problem of standard support vector machine for multiclass data classification, by introducing entropy degree and using lower and upper approximation of membership function in fuzzy rough set theory.