FS-FOIL: an inductive learning method for extracting interpretable fuzzy descriptions

被引：27

作者：

Drobics, M

Bodenhofer, U

Klement, EP

机构：

[1] Software Competence Ctr Hagenberg, A-4232 Hagenberg, Austria

[2] Johannes Kepler Univ Linz, Fuzzy Log Laboratorium Linz Hagenberg, Dept Algebra Stochast & Knowledge Based Math Syst, A-4040 Linz, Austria

来源：

INTERNATIONAL JOURNAL OF APPROXIMATE REASONING | 2003年 / 32卷 / 2-3期

关键词：

clustering; data mining; fuzzy rules; inductive learning; interpretability; machine learning;

D O I：

10.1016/S0888-613X(02)00080-4

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper is concerned with FS-FOIL - an extension of Quinlan's First-Order Inductive Learning Method (FOIL). In contrast to the classical FOIL algorithm, FS-FOIL uses fuzzy predicates and, thereby, allows to deal not only with categorical variables, but also with numerical ones, without the need to draw sharp boundaries. This method is described in full detail along with discussions how it can be applied in different traditional application scenarios - classification, fuzzy modeling, and clustering. We provide examples of all three types of applications in order to illustrate the efficiency, robustness, and wide applicability of the FS-FOIL method. (C) 2002 Elsevier Science Inc. All rights reserved.

引用

页码：131 / 152

页数：22

共 7 条

[1] FS-FOIL: An inductive learning method for extracting interpretable fuzzy descriptions
Bodenhofer, U. (ulrich.bodenhofer@scch.at), 1600, Elsevier Inc. (32): : 2 - 3
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