Induction of multiple fuzzy decision trees based on rough set technique

作     者：Wang, Xi-Zhao Zhai, Jun-Hai Lu, Shu-Xia 

作者机构：Hebei Univ Coll Math & Comp Sci Key Lab Machine Learning & Computat Intelligence Baoding 071002 Peoples R China 

出 版 物：《INFORMATION SCIENCES》 (信息科学)

年 卷 期：2008年第178卷第16期

页      面：3188-3202页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：100 excellent innovative scientists in Hebei Province Scientific Research Foundation of Hebei Province, (04213533, 06213548) National Natural Science Foundation of China, NSFC, (60473045) Natural Science Foundation of Hebei Province, (F2008000635) 

主　　题：learning rough sets fuzzy attribute reduct fuzzy decision tree induction fusion integral 

摘      要：The integration of fuzzy sets and rough sets can lead to a hybrid soft-computing technique which has been applied successfully to many fields such as machine learning, pattern recognition and image processing. The key to this soft-computing technique is how to set up and make use of the fuzzy attribute reduct in fuzzy rough set theory. Given a fuzzy information system, we may find many fuzzy attribute reducts and each of them can have different contributions to decision-making. If only one of the fuzzy attribute reducts, which may be the most important one, is selected to induce decision rules, some useful information hidden in the other reducts for the decision-making will be losing unavoidably. To sufficiently make use of the information provided by every individual fuzzy attribute reduct in a fuzzy information system, this paper presents a novel induction of multiple fuzzy decision trees based on rough set technique. The induction consists of three stages. First several fuzzy attribute reducts are found by a similarity based approach, and then a fuzzy decision tree for each fuzzy attribute reduct is generated according to the fuzzy ID3 algorithm. The fuzzy integral is finally considered as a fusion tool to integrate the generated decision trees, which combines together all outputs of the multiple fuzzy decision trees and forms the final decision result. An illustration is given to show the proposed fusion scheme. A numerical experiment on real data indicates that the proposed multiple tree induction is superior to the single tree induction based on the individual reduct or on the entire feature set for learning problems with many attributes. Crown Copyright (c) 2008 Published by Elsevier Inc. All rights reserved.