Expert pruning based on genetic algorithm in regression problems

Authors:
S. A. Jafari;S. Mashohor;Abd. R. Ramli;M. Hamiruce Marhaban
Affiliations:
Department of Computer and Communication Systems Engineering, Faculty of Engineering, University Putra Malaysia, Malaysia and Petroleum Training Center of Mahmoudabad, Mazandaran, Iran;Department of Computer and Communication Systems Engineering, Faculty of Engineering, University Putra Malaysia, Malaysia;Department of Computer and Communication Systems Engineering, Faculty of Engineering, University Putra Malaysia, Malaysia;Department of Electrical and Electronics Engineering, Faculty of Engineering, University Putra Malaysia, Malaysia
Venue:
ACIIDS'12 Proceedings of the 4th Asian conference on Intelligent Information and Database Systems - Volume Part III
Year:
2012

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Abstract

Committee machines are a set of experts that their outputs are combined to improve the performance of the whole system which tend to grow into unnecessarily large size in most of the time. This can lead to extra memory usage, computational costs, and occasional decreases in effectiveness. Expert pruning is an intermediate technique to search for a good subset of all members before combining them. In this paper we studied an expert pruning method based on genetic algorithm to prune regression members. The proposed algorithm searches to find a best subset of experts by creating a logical weight for each member and chooses which member that the related weight is equal to one. The final weights for selected experts are calculated by genetic algorithm method. The results showed that MSE and R-square for the pruned CM are 0.148 and 0.9032 respectively that are reasonable rather than all experts separately.