Rule Acquisition with an Entropy-Based Hybrid Genetic Algorithm

Author

Wan, Liyong ; Zhao, Chengling

Author_Institution

Coll. of Humanity & Social Sci., Wuhan Univ. of Sci. & Eng., Wuhan, China

Volume

2

fYear

2009

fDate

30-31 May 2009

Firstpage

275

Lastpage

278

Abstract

The paper describes the implementation and the functioning of RAHGA (Rule Acquisition with an Entropy-based Genetic Algorithm), a genetic-algorithm-based data mining system suitable for both supervised and certain types of unsupervised knowledge extraction from large and possibly noisy databases. RAHGA differs from a standard Genetic Algorithm. We compared our approach with several other traditional data mining techniques. The results show that our approach outperformed others on both the prediction accuracy and the standard deviation.

Keywords

data mining; entropy; genetic algorithms; unsupervised learning; data mining system; entropy-based hybrid genetic algorithm; large database; noisy database; rule acquisition; unsupervised knowledge extraction; Biological cells; Data engineering; Data mining; Databases; Educational institutions; Gas insulated transmission lines; Genetic algorithms; Genetic engineering; Genetic mutations; Knowledge engineering; data mining; entropy; genetic algorithm; knowledge extraction;

fLanguage

English

Publisher

ieee

Conference_Titel

Networking and Digital Society, 2009. ICNDS '09. International Conference on

Conference_Location

Guiyang, Guizhou

Print_ISBN

978-0-7695-3635-4

Type

conf

DOI

10.1109/ICNDS.2009.148

Filename

5116737