Feature Selection with Attributes Clustering by Maximal Information Coefficient
Xi Zhao, Wei Deng, Yong Shi · Procedia Computer Science · 2013
Feature selection is usually a separate procedure which can not benefit from result of the data exploration. In this paper, we propose a unsupervised feature selection method which could reuse a specific data exploration result. Furthermore, our algorithm follows the idea of clustering attributes and combines two state-of-the-art data analyzing methods, that's maximal information coefficient and affinity propagation. Classification problems with different classifiers were tested to validation our method and others. Data experiments result exhibits our unsupervised algorithm is comparable with classical feature selection methods and even outperforms some supervised learning algorithms. Data simulation with one credit dataset of our own from a bank of China shows the capability of our method for real world application.