Fast and accurate feature selection using hybrid genetic strategies
César Guerra-Salcedo, Stephen Y. Chen, Darrell Whitley, Stephen F. Smith · 2003
When dealing with object classification, each object is defined by a set of features (characteristics) that classify the object to a particular class. The problem is how to choose the best subset of characteristics that provide an accurate classification. Previous research has shown that decision tables are as accurate as C4.5 for classification purposes. Two different genetic search techniques, CHC and CF/RSC, are applied to this problem. Results shows that CF/RSC and decision tables are a very good combination when dealing with large feature spaces. Results also suggest that CHC is better when used for problems with noise added to the features.