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Computational Intelligence In Manufacturing Handbook P19

Inductive learning or classification of objects from large-scale empirical data sets is an important research area in artificial intelligence (AI). In recent years, many techniques have been developed to perform inductive learning. Among them, the decision tree learning technique is the most popular. | Khoo Li-Pheng et al RClass A Prototype Rough-Set and Genetic Algorithms Enhanced Multi-Concept Classification System for Manufacturing Diagnosis Computational Intelligence in Manufacturing Handbook Edited by Jun Wang et al Boca Raton CRC Press LLC 2001 19 RClass A Prototype Rough-Set and Genetic Algorithms Enhanced Multi-Concept Classification System for Manufacturing Diagnosis 19.1 Introduction 19.2 Li-Pheng Khoo 19 3 Nanyang Technological University . Lian-Yin Zhai 19.5 Nanyang Technological University 19.6 Basic Notions A Prototype Multi-Concept Classification System Validation of RClass Application of RClass to Manufacturing Diagnosis Conclusions 19.1 Introduction Inductive learning or classification of objects from large-scale empirical data sets is an important research area in artificial intelligence AI . In recent years many techniques have been developed to perform inductive learning. Among them the decision tree learning technique is the most popular. Using such a technique Quinlan 1992 has successfully developed the Inductive Dichotomizer 3 ID3 and its later versions C4.5 and C5.0 See 5.0 in 1986 1992 and 1997 respectively. Essentially decision support is based on human knowledge about a specific part of a real or abstract world. If the knowledge is gained by experience decision rules can possibly be induced from the empirical training data obtained. In reality due to various reasons empirical data often has the property of granularity and may be incomplete imprecise or even conflicting. For example in diagnosing a manufacturing system the opinions of two engineers can be different or even contradictory. Some earlier inductive learning systems such as the once prevailing decision tree learning system the ID3 are unable to deal with imprecise and inconsistent information present in empirical training data Khoo et al. 1999 . Thus the ability to handle imprecise and inconsistent information has become one of the most important requirements for a .