An Exponential Monte-Carlo algorithm for feature selection problems

Authors:
Salwani Abdullah;Nasser R. Sabar;Mohd Zakree Ahmad Nazri;Masri Ayob
Affiliations:
Data Mining and Optimization Research Group (DMO), Center for Artificial Intelligence Technology (CAIT), Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor, Malaysia;The University of Nottingham Malaysia Campus, Jalan Broga, 43500 Semenyih, Selangor, Malaysia;Data Mining and Optimization Research Group (DMO), Center for Artificial Intelligence Technology (CAIT), Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor, Malaysia;Data Mining and Optimization Research Group (DMO), Center for Artificial Intelligence Technology (CAIT), Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor, Malaysia
Venue:
Computers and Industrial Engineering
Year:
2014

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Abstract

Feature selection problems (FS) can be defined as the process of eliminating redundant features while avoiding information loss. Due to that fact that FS is an NP-hard problem, heuristic and meta-heuristic approaches have been widely used by researchers. In this work, we proposed an Exponential Monte-Carlo algorithm (EMC-FS) for the feature selection problem. EMC-FS is a meta-heuristic approach which is quite similar to a simulated annealing algorithm. The difference is that no cooling schedule is required. Improved solutions are accepted and worse solutions are adaptively accepted based on the quality of the trial solution, the search time and the number of consecutive non-improving iterations. We have evaluated our approach against the latest methodologies in the literature on standard benchmark problems. The quality of the obtained subset of features has also been evaluated in terms of the number of generated rules (descriptive patterns) and classification accuracy. Our research demonstrates that our approach produces some of the best known results.