Feature Subset Selection by Neuro-rough Hybridization

Abstract

Feature subset selection is of prime importance in pattern classification, machine learning and data mining applications. Though statistical techniques are well developed and mathematically sound, they are inappropriate for dealing real world cognitive problems containing imprecise and ambiguous information. Soft computing tools like artificial neural network, genetic algorithm fuzzy logic, rough set theory and their integration in developing hybrid algorithms for handling real life problems are recently found to be the most effective. In this work a neuro- rough hybrid algorithm has been proposed in which rough set concepts are used for finding an initial subset of efficient features followed by a neural stage to find out the ultimate best feature subset. The reduction of original feature set results in a smaller structure and quicker learning of the neural stage and as a whole the hybrid algorithm seems to provide better performance than any algorithm from individual paradigm as is evident from the simulation results.