Spatial distance join based feature selection

A Spatial Distance Join (SDJ) based feature selection method (SDJ-FS) is developed to extend the concept of Correlation Fractal Dimension (CFD) to handle both feature relevance and redundancy jointly for supervised feature selection problems. The Pair-count Exponents (PCEs) for the SDJ between different classes and that of the entire dataset (i.e., the CFD of the dataset) are proposed respectively as feature relevance and redundancy measures. For the SDJ-FS method, an efficient divide-count approach of backward elimination property is designed for the calculation of the SDJ based feature quality (relevance and redundancy) measures. The extensive evaluations on both synthetic and benchmark datasets demonstrate the capability of SDJ-FS in identification of feature subsets of high relevance and low redundancy, along with the favorable performance of SDJ-FS over other reference feature selection methods (including those based on CFD). The success of SDJ-FS shows that, SDJ provides a good framework for the extension of CFD to supervised feature selection problems and offers a new view point for feature selection researches.