Effective Classification with Hybrid Evolutionary Techniques

Author

Jaganathan, P. ; Thangavel A., K. ; Pethalakshmi, A.

Author_Institution

PSNA Coll. of Eng. & Tech., Dindigul

fYear

2006

fDate

20-23 Dec. 2006

Firstpage

335

Lastpage

338

Abstract

Ant colony optimization (ACO) algorithms have been applied successfully to combinatorial optimization problems. More recently, Parpinelli et al have applied ACO to data mining classification problems, where they introduced a classification algorithm called Ant Miner. In this paper, we present a hybrid system that combines both the proposed Enhanced Quickreduct algorithm for data preprocessing and ant miner. The system was tested on standard data set and its performance is better than the original Ant Miner algorithm.

Keywords

artificial life; data mining; evolutionary computation; learning (artificial intelligence); optimisation; pattern classification; rough set theory; ant colony optimization; ant miner; combinatorial optimization problem; data mining classification; data preprocessing; enhanced quickreduct algorithm; hybrid evolutionary technique; Ant colony optimization; Art; Classification algorithms; Data mining; Data preprocessing; Databases; Educational institutions; Human immunodeficiency virus; Rough sets; Testing; Ant Colony Optimization(ACO); Classification; Enhanced Quick Reduct Algorithm; Quick Reduct;

fLanguage

English

Publisher

ieee

Conference_Titel

Advanced Computing and Communications, 2006. ADCOM 2006. International Conference on

Conference_Location

Surathkal

Print_ISBN

1-4244-0716-8

Electronic_ISBN

1-4244-0716-8

Type

conf

DOI

10.1109/ADCOM.2006.4289911

Filename

4289911