Feature Selection by Iterative Block Addition and Block Deletion

In our previous work, we proposed feature selection by block addition (BA) and block deletion (BD). In this paper, to further reduce features, we iterate BABD until no features are eliminated. In our method, we add several features at a time to the feature set until a stopping condition is satisfied. Then we delete features that do not deteriorate the selection criterion by block deletion. We iterate block addition and block deletion for the selected feature set until no features are eliminated. By computer experiments using micro array data sets we show that for some micro array data sets, features are further deleted by iterating BABD and as the selection and ranking criteria the weighted sum of the recognition error rate and the average of margin errors is better than the recognition error rate in obtaining a feature set with high generalization ability.