A novel hybrid feature selection via Symmetrical Uncertainty ranking based local memetic search algorithm

作者：Kannan S Senthamarai^*; Ramaraj N

来源：Knowledge-Based Systems, 2010, 23(6): 580-585.

DOI：10.1016/j.knosys.2010.03.016

• Correlation based memetic search
• Symmetrical Uncertainty ranking
• Hybrid feature selection

A novel correlation based memetic framework (MA-C) which is a combination of genetic algorithm (GA) and local search (LS) using correlation based filter ranking is proposed in this paper. The local filter method used here fine-tunes the population of GA solutions by adding or deleting features based on Symmetrical Uncertainty (SU) measure The focus here is on filter methods that are able to assess the goodness or ranking of the individual features Empirical study of MA-C on several commonly used datasets from the large-scale Gene expression datasets indicates that it outperforms recent existing methods in the literature in terms of classification accuracy, select

• 出版日期2010-8