A hybrid classification algorithm evaluated on medical data
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. © 2010 IEEE.