Discriminative Feature Selection System Using Active Mining Technique

摘要

A discriminative feature set (DFS) selection method is described wherein a forward wrapper framework and a self error-correction concept are used. In this approach, the first feature is selected using a statistical measure. After that, the feature that aims to correct the errors made by the current feature set is selected using a measure called correction score (CS) and is subsequently added into the feature set. This error-corrective feature-adding process stops until a required number of features are included into the DFS or a pre-defined accuracy is achieved. According to different levels of error correction, this method has three derivatives for different tasks and data. The speediness and adaptability of this approach make it efficient and effective for high-dimensional discriminative feature selection.