Transforming input variables for RBFN based on PCA-ASH multivariate correlation analysis and its application

Abstract

The mutual information (MI) based on averaged shifted histogram (ASH) probability density estimator is considered as a good indicator of relevance between input variables and output variable. However, it cannot deal with redundant input variables problem. Therefore, a method integrates principal component analysis (PCA) with MI is proposed for radial basis function network (RBFN) to improve the predicting performance of RBFN. Firstly, PCA is employed to characterize the PCs from original variables, among which there is non-correlation. Secondly, MI based on ASH is applied to select the several closest correlation PCs with output variable as the new input variables. Finally, PCA-ASH-RBFN is employed to develop the housing price model based on the Boston housing data set. The result shows that PCA-ASH-RBFN has better prediction and robust performance than PCA-RBFN and RBFN integrating with robust feature selection for input variables.