Obtaining interpretable fuzzy classification rules from medical data

被引:268
作者
Nauck, D [1 ]
Kruse, R [1 ]
机构
[1] Univ Magdeburg, Fac Comp Sci FIN IWS, D-39106 Magdeburg, Germany
关键词
classification; learning; neuro-fuzzy system; rule based classifier;
D O I
10.1016/S0933-3657(98)00070-0
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
For many application problems classifiers can be used to support a decision making process. In some domains-in areas like medicine especially-it is preferable not to use black box approaches. The user should be able to understand the classifier and to evaluate its results. Fuzzy rule based classifiers are especially suitable, because they consist of simple linguistically interpretable rules and do not have some of the drawbacks of symbolic or crisp rule based classifiers. Classifiers must often be created from data by a learning process, because there is not enough expert knowledge to determine their parameters completely. A simple and convenient way to learn fuzzy classifiers from data is provided by neuro-fuzzy approaches. In this paper we discuss extensions to the learning algorithms of neuro-fuzzy classification (NEFCLASS), a neuro-fuzzy approach for data analysis that we have presented before. We present interactive strategies for pruning rules and variables from a trained classifier to enhance its readability, and demonstrate our approach on a small example. (C) 1999 Elsevier Science B.V, All rights reserved.
引用
收藏
页码:149 / 169
页数:21
相关论文
共 24 条
[1]   A METHOD FOR FUZZY RULES EXTRACTION DIRECTLY FROM NUMERICAL DATA AND ITS APPLICATION TO PATTERN-CLASSIFICATION [J].
ABE, S ;
LAN, MS .
IEEE TRANSACTIONS ON FUZZY SYSTEMS, 1995, 3 (01) :18-28
[2]  
[Anonymous], P 1995 ACM S APPL CO
[3]  
[Anonymous], Pattern Recognition With Fuzzy Objective Function Algorithms
[4]   LEARNING AND TUNING FUZZY-LOGIC CONTROLLERS THROUGH REINFORCEMENTS [J].
BERENJI, HR ;
KHEDKAR, P .
IEEE TRANSACTIONS ON NEURAL NETWORKS, 1992, 3 (05) :724-740
[5]   Now comes the time to defuzzify neuro-fuzzy models [J].
Bersini, H ;
Bontempi, G .
FUZZY SETS AND SYSTEMS, 1997, 90 (02) :161-169
[6]  
BERSINI H, 1997, P 7 INT FUZZ SYST AS, V2, P354
[7]  
Bezdek JC., 1992, FUZZY MODELS PATTERN
[8]  
Brown M, 1994, NEUROFUZZY ADAPTIVE
[9]   NEURAL NETS FOR FUZZY-SYSTEMS [J].
BUCKLEY, JJ ;
HAYASHI, Y .
FUZZY SETS AND SYSTEMS, 1995, 71 (03) :265-276
[10]   FUZZY NEURAL NETWORKS - A SURVEY [J].
BUCKLEY, JJ ;
HAYASHI, Y .
FUZZY SETS AND SYSTEMS, 1994, 66 (01) :1-13