FSSC: An Algorithm for Classifying Numerical Data Using Fuzzy Soft Set Theory

Authors:
Bana Handaga;Tutut Herawan;Mustafa Mat Deris
Affiliations:
Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Johor, Malaysia;Department of Computer Science, Faculty of Computer Systems and Software Engineering, Universiti Malaysia Pahang, Kuantan Pahang, Malaysia;Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Johor, Malayisa
Venue:
International Journal of Fuzzy System Applications
Year:
2012

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Abstract

Introduced is a new algorithm for the classification of numerical data using the theory of fuzzy soft set, named Fuzzy Soft Set Classifier FSSC. The algorithm uses the fuzzy approach in the pre-processing stage to obtain features, and similarity concept in the process of classification. It can be applied not only to binary-valued datasets, but also be able to classify the data that consists of real numbers. Comparison tests on seven datasets from UCI Machine Learning Repository have been carried out. It is shown that the proposed algorithm provides better accuracy and higher accuracy as compared to the baseline algorithm using soft set theory.