Comparative Study among Data Reduction Techniques over Classification Accuracy

Abstract

Nowadays, Healthcare is one of the most critical issues that need efficient and effective analysis. Data mining provides many techniques and tools that help in getting a good analysis for healthcare data. Data classification is a form of data analysis for deducting models. Mining on a reduced version of data or a lower number of attributes increases the efficiency of system providing almost the same results. In this paper, a comparative study between different data reduction techniques is introduced. Such comparison is tested against classification algorithms accuracy. The results showed that fuzzy rough feature selection outperforms rough set attribute selection, gain ratio, correlation feature selection and principal components analysis.