Feature evaluation based Fuzzy C-Mean classification

Fuzzy C-Means Clustering, FCM, is an iterative algorithm whose aim is to find the center or centroid of data clusters that minimize an assigned dissimilarity function. The degree of being in a certain cluster can be defined in terms of the distance to the cluster-centroid. The domain knowledge is used to formulate an appropriate measure. However the Euclidean distance is considered as a general measure for such value. The calculation of the Euclidean distance doesn´t take into consideration the degree of relevance of each feature to the classification model. In this paper, scoring methods like ChiMerge and Mutual information are used in the FCM model to improve the calculation of the Euclidean distance. Experimental results demonstrate the better performances of the improved FCM on UCI benchmark data sets rather than the ordinary FCM, where the ordinary FCM uses in classification either all features or the most important features while the improved FCM uses all the features but the Euclidean Distance will be calculated according to the relevance degree of each feature.