Data Mining Using Learning Automata

Authors: M. R. Aghaebrahimi, S. H. Zahiri, M. Amiri

Abstract:

In this paper a data miner based on the learning automata is proposed and is called LA-miner. The LA-miner extracts classification rules from data sets automatically. The proposed algorithm is established based on the function optimization using learning automata. The experimental results on three benchmarks indicate that the performance of the proposed LA-miner is comparable with (sometimes better than) the Ant-miner (a data miner algorithm based on the Ant Colony optimization algorithm) and CNZ (a well-known data mining algorithm for classification).

Keywords: Data mining, Learning automata, Classification rules, Knowledge discovery.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1062358

References:

[1] B. J. Oommen, E.V. de St. Criox, "Graph partitioning using learning automata," IEEE Trans. Comput., vol. 45, pp. 195-208, 1996.
[2] H. Beigy, M.R. Meybodi, "Backpropagation algorithm adaptation parameters using learning automata," Int. J. Neural Syst., vol. 11, pp.219-228, 2001.
[3] S.H. Zahiri, "Learning automata based classifier," Pattern Recognition Letters, vol. 9, pp.40-48, 2008.
[4] R.S. Parepinelli, H.S. Lopes, A. Freitas, "An ant colony algorithm for classification rules discovery," IEEE Trans. Evol. Comp., vol. 6, No.4, pp.321-332, 2002.
[5] P. Clark, T.Niblet, "The CNZ induction algorithm," Mach. Learn., vol.3, no.4, pp.261-283, 1989.