K nearest neighbor reinforced expectation maximization method

作     者：Aci, Mehmet Avci, Mutlu 

作者机构：Cukurova Univ Dept Comp Engn Adana Turkey 

出 版 物：《EXPERT SYSTEMS WITH APPLICATIONS》 (专家系统及其应用)

年 卷 期：2011年第38卷第10期

页      面：12585-12591页

核心收录：

学科分类：1201[管理学-管理科学与工程(可授管理学、工学学位)] 0808[工学-电气工程] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：K nearest neighbor method Bayesian method Expectation maximization algorithm Hybrid method Classification Clustering 

摘      要：K nearest neighbor and Bayesian methods are effective methods of machine learning. Expectation maximization is an effective Bayesian classifier. In this work a data elimination approach is proposed to improve data clustering. The proposed method is based on hybridization of k nearest neighbor and expectation maximization algorithms. The k nearest neighbor algorithm is considered as the preprocessor for expectation maximization algorithm to reduce the amount of training data making it difficult to learn. The suggested method is tested on well-known machine learning data sets iris, wine, breast cancer, glass and yeast. Simulations are done in MATLAB environment and performance results are concluded. (C) 2011 Elsevier Ltd. All rights reserved.