A Study of Classification Algorithm for Data Mining Based on Hybrid Intelligent Systems

Authors:
Gang Wang;Chenghong Zhang;Lihua Huang
Affiliations:
-;-;-
Venue:
SNPD '08 Proceedings of the 2008 Ninth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing
Year:
2008

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Abstract

Facing the huge amounts of data, the familiar classification algorithms show the shortages on time efficiency, robustness and accuracy. So this article puts the Hybrid Intelligent Systems into the research of classification algorithm. Based on the cognitive psychology and aggregative model theory, the article proposes a new Hybrid Intelligent System: R-FC-DENN, according to Rough Set, Clustering theory, Fuzzy Logic, Genetic Algorithm and Artificial Neural Network. Firstly, R-FC-DENN uses the Rough Set to reduce the data. And then it clusters the data by the Clustering theory. After that, it uses different and improved ANN to train. Subsequently, the data which are trained are integrated by fuzzy power. Lastly, the integrated data are trained by another improved ANN and the whole process of training is completed. In the end, experiments are carried out based on the data of UCI database and it is observed that the system is valid.