An Effective Feature Selection Scheme for Healthcare Data Classification Using Binary Particle Swarm Optimization

Feature selection (FS) is one of fundamental data processing techniques in various machine learning algorithms, especially for classification of healthcare data. However, it is a challenging issue due to the large search space. This paper proposed a confidence based and cost effective feature selection method using binary particle swarm optimization, CCFS. First, CCFS improves search effectiveness by developing a new updating mechanism, in which confidence of each feature is explicitly considered, including the correlation between feature and categories, and historically selected frequency of each feature. Second, the classification accuracy, the feature reduction ratio, and the feature cost are comprehensively incorporated into the design of the fitness function. The proposed method has been verified in UCI cancer classification dataset (Lung Cancer). The experimental result shows the effectiveness of the proposed method, in terms of accuracy and feature selection cost.