A new ensemble feature selection and its application to pattern classification

作     者：Dongbo ZHANG Yaonan WANG 

作者机构：Institute of Information Engineering Xiangtan University Xiangtan Hunan 411105 China College of Electrical and Information Engineering Hunan University Changsha Hunan 410082 China 

出 版 物：《控制理论与应用（英文版）》 (JOURNAL OF CONTROL THEORY AND APPLICATIONS)

年 卷 期：2009年第7卷第4期

页      面：419-426页

核心收录：

学科分类：12[管理学] 13[艺术学] 0808[工学-电气工程] 08[工学] 1305[艺术学-设计学（可授艺术学、工学学位）] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 0804[工学-仪器科学与技术] 0802[工学-机械工程] 0835[工学-软件工程] 081101[工学-控制理论与控制工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the National High-Tech Research and Development Plan of China (No.2007AA04Z224) the National Natural Science Foundation of China (No.60775047, 60835004) 

主　　题：Rough sets reduction Ensemble feature selection Neural network ensemble Remote sensing image classification 

摘      要：Neural network ensemble based on rough sets reduct is proposed to decrease the computational complexity of conventional ensemble feature selection algorithm. First, a dynamic reduction technology combining genetic algorithm with resampling method is adopted to obtain reducts with good generalization ability. Second, Multiple BP neural networks based on different reducts are built as base classifiers. According to the idea of selective ensemble, the neural network ensemble with best generalization ability can be found by search strategies. Finally, classification based on neural network ensemble is implemented by combining the predictions of component networks with voting. The method has been verified in the experiment of remote sensing image and five UCI datasets classification. Compared with conventional ensemble feature selection algorithms, it costs less time and lower computing complexity, and the classification accuracy is satisfactory.