An Empirical Comparison between Global and Greedy-like Search for Feature Selection

The paper presents a comparison between two feature selection methods; the Importance Score (IS) and a genetic algorithm-based (GA) method. The goal of both is to achieve better performing rules produced by the AQ15 learning system. The IS method performs a greedy-like search based on an attributional score that represents the importance of each attribute in classifying the decision classes. IS uses the rule testing system Atest to evaluate the performance of the selected feature sets. The genetic algorithm method explores, in an efficient way, the space of all possible subsets to obtain the set of features that maximizes the predictive accuracy of the learned rules. The GA method uses the GENESIS system to globally search the space. It uses an Evaluation Function for providing a feedback about the fitness of each feature subset. The comparison is done on three real world problems, wind bracing design, accident data, and Soybean data.