Rule extraction from expert heuristics: A comparative study of rough sets with neural networks and ID3

被引：66

作者：

Mak, B ^{[1
]}

Munakata, T

机构：

[1] San Francisco State Univ, Coll Business, Dept Informat Syst & Business Anal, San Francisco, CA 94132 USA

[2] Cleveland State Univ, Dept Comp & Informat Sci, Cleveland, OH 44115 USA

来源：

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH | 2002年 / 136卷 / 01期

关键词：

rough sets; neural networks; heuristics; rule extraction;

D O I：

10.1016/S0377-2217(01)00062-5

中图分类号：

C93 [管理学];

学科分类号：

12 ; 1201 ; 1202 ; 120202 ;

摘要：

The rule extraction capability of neural networks is an issue of interest to many researchers. Even though neural networks offer high accuracy in classification and prediction, there are criticisms on the complicated and non-linear transformation performed in the hidden layers. It is difficult to explain the relationships between inputs and outputs and derive simple rules governing the relationships between them. As alternatives, some researchers recommend the use of rough sets or ID3 for rule extraction. This paper reviews and compares the rule extraction capabilities of rough sets with neural networks and ID3. We apply the methods to analyze expert heuristic judgments. Strengths and weaknesses of the methods are compared, and implications for the use of the methods are suggested. (C) 2002 Elsevier Science B.V. All rights reserved.

引用

页码：212 / 229

页数：18

共 39 条

[21] Learning from linguistic rules and rule extraction for function approximation by neural networks
Tanaka, K
Nii, M
Ishibuchi, H
[J]. SIMULATED EVOLUTION AND LEARNING, 1999, 1585 : 317 - 324
[22] Reduction of input vectors by using of attribute selection in rule extraction method from trained neural networks
Nii, M
Ogino, K
[J]. SICE 2003 ANNUAL CONFERENCE, VOLS 1-3, 2003, : 1576 - 1581
[23] Computer-Aided Heart Disease Diagnosis Using Recursive Rule Extraction Algorithms from Neural Networks
Chakraborty, Manomita
Biswas, Saroj Kumar
[J]. INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE AND APPLICATIONS, 2022, 21 (02)
[24] Rule extraction from knowledge-based neural networks with adaptive inductive bias
Snyders, S
Omlin, CW
[J]. 8TH INTERNATIONAL CONFERENCE ON NEURAL INFORMATION PROCESSING, VOLS 1-3, PROCEEDING, 2001, : 143 - 148
[25] Rule extraction from trained adaptive neural networks using artificial immune systems
Kahramanli, Humar
Allahverdi, Novruz
[J]. EXPERT SYSTEMS WITH APPLICATIONS, 2009, 36 (02) : 1513 - 1522
[26] Rule extraction from artificial neural networks to discover causes of quality defects in fabric production
Ozbakir, Lale
Baykasoglu, Adil
Kulluk, Sinem
[J]. NEURAL COMPUTING & APPLICATIONS, 2011, 20 (07) : 1117 - 1128
[27] Rule extraction from artificial neural networks to discover causes of quality defects in fabric production
Lale Özbakır
Adil Baykasoğlu
Sinem Kulluk
[J]. Neural Computing and Applications, 2011, 20 : 1117 - 1128
[28] A penalty-function approach to rule extraction from knowledge-based neural networks
de Menezes, RM
Zaverucha, G
Barbosa, VC
[J]. ICONIP'98: THE FIFTH INTERNATIONAL CONFERENCE ON NEURAL INFORMATION PROCESSING JOINTLY WITH JNNS'98: THE 1998 ANNUAL CONFERENCE OF THE JAPANESE NEURAL NETWORK SOCIETY - PROCEEDINGS, VOLS 1-3, 1998, : 1497 - 1500
[29] Rule Extraction from Neural Networks Via Ant Colony Algorithm for Data Mining Applications
Ozbakir, Lale
Baykasoglu, Adil
Kulluk, Sinem
[J]. LEARNING AND INTELLIGENT OPTIMIZATION, 2008, 5313 : 177 - +
[30] Fuzzy logic and evolutionary algorithm-two techniques in rule extraction from neural networks
Markowska-Kaczmar, U
Trelak, W
[J]. NEUROCOMPUTING, 2005, 63 : 359 - 379

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