FEATURE SELECTION BASED ON FUZZY ENTROPY
B. Azhagusundari, Pollachi Coimbatore · 2013
The attribute reduction is one of the key processes for knowledge acquisition. Some data set is multidimensional and larger in size. When this data set is used for classification it may produce wrong results and it may also occupy more resources especially in terms of time. Most of the features present are redundant, inconsistent and affects the classification. To improve the efficiency of classification these redundant and inconsistent nature must be eliminated. This paper presents a new method for dealing with feature subset selection based on fuzzy entropy measures for handling classification problems. The first step is to discretize numeric data to construct the membership function of each fuzzy set of a feature. Then, select the feature subset based on the proposed fuzzy entropy measure focusing on boundary samples. The Paper also gives an experimental result to show the applicability of the proposed method. The performance of the system is evaluated in MATLAB on several benchmark data sets with resides in the UCI machine learning repository.