Disjunctive form concept learning system based on genetic algorithm

Author

Endo, Satoshi ; Ohuchi, Azuma

Author_Institution

Fac. of Eng., Ryukyus Univ., Okinawa, Japan

Volume

5

fYear

1995

fDate

22-25 Oct 1995

Firstpage

4696

Abstract

“Version Space” proposed by Mitchell (1977) is a typical method of concept learning from training examples, but this method has some points to be improved. The purpose of this paper is to construct a flexible learning mechanism which can be applied to critical points. In this paper to do this, the method of concept learning based on genetic algorithms (GA) is proposed. The important features of the algorithm are as follows. Firstly, the system is able to learn the target concept formed by disjunctive normal forms (DNF). Secondly, if there are some incorrect examples in the training examples set, the algorithm will reduce them and generate correct the target concept. This function is called “noise reduction”. Finally, the algorithm is able to learn the target concept from only positive example set

Keywords

genetic algorithms; inference mechanisms; learning by example; concept learning; disjunctive form concept learning system; flexible learning mechanism; genetic algorithm; noise reduction; Genetic algorithms; Genetic engineering; Inference algorithms; Inference mechanisms; Law; Learning systems; Legal factors; Machine learning; Machine learning algorithms; Training data;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems, Man and Cybernetics, 1995. Intelligent Systems for the 21st Century., IEEE International Conference on

Conference_Location

Vancouver, BC

Print_ISBN

0-7803-2559-1

Type

conf

DOI

10.1109/ICSMC.1995.538537

Filename

538537