Abstract: In this study, we propose an efficient hybrid feature subset selection method to overcome the curse of dimensionality and to obtain good learning performance on classification problems. The proposed method includes two steps: Scheming an evaluation function to create the rank of the feature significance, constructing a new Binary Search Feature Subset (BSFS) algorithm to generate the optimum feature subset. We have applied the proposed method on a Modular Perceptron Network (MPN) to learn the realworld datasets. It shows that from the experimental results the feature of the input data can be decreased largely (less 75%~88%), the data presentations are reduced (less 67%~ 91%) and a small size MPNs can be procured with learning and testing performance maintained as the good level as before.
Yen-Po Lee , Wei-Yu Han , Wu-Ja Lin and Kuang-Shyr Wu , 2006. A Hybrid Feature Selection Method Using Modular Perceptron Networks . Asian Journal of Information Technology, 5: 1088-1094.