A Numerical Classification Technique Based on Fuzzy Soft Set Using Hamming Distance

Abstract

In recent decades, fuzzy soft set techniques and approaches have received a great deal of attention from practitioners and soft computing researchers. This article attempts to introduce a classifier for numerical data using similarity measure fuzzy soft set (FSS) based on Hamming distance, named HDFSSC. Dataset have been taken from UCI Machine Learning Repository and MIAS (Mammographic Image Analysis Society). The proposed modeling consists of four phases: data acquisition, feature fuzzification, training phase and testing phase. Later, head to head comparison between state of the art fuzzy soft set classifiers is provided. Experiment results showed that the proposed classifier provides better accuracy when compared to the baseline fuzzy soft set classifiers.