An Experimental Study An Feature Selection Methods And Development Of New Scaleup Algorithm
This paper proposes a novel feature selection using minimum variance method. The purpose of the proposed method is to reduce the computational complexity, reduce the number of initial features and increase the classification accuracy of the selected feature subsets. The clusters are formed using minimum variance method. The process must be repeated for different pairs of records and voting is done on the different sets of cluster pairs. The cluster pair which has the maximum number of votes is chosen. Now the highest priority member is chosen from each cluster using information gain and removing the remaining attributes, thus reducing the number of attributes. The paper also evaluates the approach by comparing it with existing feature selection algorithms over 9 datasets from UCI and WebKb Datasets. The proposed method shows better results in terms of number of selected features, classification accuracy, and running time than most existing algorithms.
Keywords - Data Mining, Classification, Feature Selection, Dimensionality, Reduction, Minimum Variance Method, Information Gain.