Combining extension matrix and integer programming for optimal concept learning

Hang, Xiaoshu and Dai, Honghua 2004, Combining extension matrix and integer programming for optimal concept learning, Lecture notes in computer science, vol. 3157/2004, pp. 352-360.

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Title Combining extension matrix and integer programming for optimal concept learning
Author(s) Hang, Xiaoshu
Dai, HonghuaORCID iD for Dai, Honghua
Journal name Lecture notes in computer science
Volume number 3157/2004
Start page 352
End page 360
Publisher Springer-Verlag
Place of publication Berlin , Germany
Publication date 2004
ISSN 0302-9743
Summary This paper proposes two integer programming models and their GA-based solutions for optimal concept learning. The models are built to obtain the optimal concept description in the form of propositional logic formulas from examples based on completeness, consistency and simplicity. The simplicity of the propositional rules is selected as the objective function of the integer programming models, and the completeness and consistency of the concept are used as the constraints. Considering the real-world problems that certain level of noise is contained in data set, the constraints in model 11 are slacked by adding slack-variables. To solve the integer programming models, genetic algorithm is employed to search the global solution space. We call our approach IP-AE. Its effectiveness is verified by comparing the experimental results with other well- known concept learning algorithms: AQ15 and C4.5.
Language eng
Field of Research 080699 Information Systems not elsewhere classified
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2004, Springer-Verlag Berlin Heidelberg
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