Feature selection using fuzzy entropy measures with similarity classifier
Feature selection plays an important role in classification for several reasons. First it can simplify the model and this way computational cost can be reduced and also when the model is taken for practical use fewer inputs are needed which means in practice that fewer measurements from new samples are needed. Second by removing insignificant features from the data set one can also make the model more transparent and more comprehensible, providing better explanation of suggested diagnosis, which is an important requirement in medical applications. Feature selection process can also reduce noise and this way enhance the classification accuracy. In this article, feature selection method based on fuzzy entropy measures is introduced and it is tested together with similarity classifier. Model was tested with four medical data sets which were, dermatology, Pima-Indian diabetes, breast cancer and Parkinsons data set. With all the four data sets, we managed to get quite good results by using fewer features that in the original data sets. Also with Parkinsons and dermatology data sets, classification accuracy was managed to enhance significantly this way. Mean classification accuracy with Parkinsons data set being 85.03% with only two features from original 22. With dermatology data set, mean accuracy of 98.28% was achieved using 29 features instead of 34 original features. Results can be considered quite good. (C) 2010 Elsevier Ltd. All rights reserved.
feature selection similarity classification medical data fuzzy measures