A Hybrid Classification Algorithm Evaluated on Medical Data

Author

Michelakos, Ioannis ; Papageorgiou, Elpiniki ; Vasilakopoulos, Michael

Author_Institution

Dept. of Comput. Sci. & Biomed. Inf., Univ. of Central Greece, Lamia, Greece

fYear

2010

fDate

28-30 June 2010

Firstpage

98

Lastpage

103

Abstract

Ant colony optimization algorithms have been applied successfully to data mining classification problems. Recently, an improved version of cAnt-Miner (Ant-Miner coping with continuous attributes), called cAnt-Miner2, has been introduced for mining classification rules. In this paper, a hybrid algorithm is presented, combining the cAnt-Miner2 and the mRMR feature selection algorithms. The proposed algorithm was experimentally compared to cAnt-Miner2, using some public medical data sets to demonstrate its functioning. The experiments were very promising and the proposed approach is better in terms of accuracy, simplicity and computational cost than the original cAnt-Miner2 algorithm.

Keywords

data mining; medical administrative data processing; optimisation; pattern classification; ant colony optimization algorithm; cAnt-Miner; classification rules mining; data mining classification problem; hybrid classification algorithm; mRMR feature selection algorithm; public medical data sets; Ant colony optimization; Biomedical informatics; Classification algorithms; Computational efficiency; Computer science; Data mining; Diversity reception; Educational technology; Mutual information; Robustness; Ant Colony Optimization (ACO); Classification; Max-Relevance and Min-Redundancy (mRMR); Medical data; Mutual Information;

fLanguage

English

Publisher

ieee

Conference_Titel

Enabling Technologies: Infrastructures for Collaborative Enterprises (WETICE), 2010 19th IEEE International Workshop on

Conference_Location

Larissa

ISSN

1524-4547

Print_ISBN

978-1-4244-7216-1

Type

conf

DOI

10.1109/WETICE.2010.22

Filename

5541996