Optimal Feature Subset Selection using Ant Colony Optimization

Background/Objectives: Data mining is the process of extracting large volumes of raw data from hidden knowledge. The health care industry requires the use of data mining techniques as it generates huge and complex volumes of data. The applications of data mining techniques to medical data extract patterns which are useful for diagnosis, prognoses and treatment of diseases. This extraction of patterns allows doctors and hospitals to be more effective and more efficient. The huge volume of data is the barrier in the detection of patterns. Feature selection techniques mainly used in data preprocessing for data mining. Methods/Statistical Analysis: Classification task leads to reduction of the dimensionality of feature space, feature selection process is used for selecting large set of features. The Ant Colony Optimization based feature selection method is applied on cancer datasets. Findings: This research work proposes about feature selection mechanism based on Ant Colony Optimization. In an ACO algorithm, the activities of ants have significance for solving different combinatorial optimization problem which selects most relevant features. Through several iterations filter based method finds the optimal feature subset. Based on the similarity between features the feature relevance will be computed, that shows to the minimization of the redundancy. To validate the proposed feature selection method Support Vector Machine classification is applied. The accuracy of classification for whole feature set and the reduced feature subset are compared. The improved accuracy proves that the proposed feature selection approach has selected informative feature of the cancer datasets. Applications/Improvements: The possibilities of using PSO algorithm is applied for finding the best features in future. Other algorithms are also considered for further implementation.