A GA-based fuzzy feature evaluation algorithm for pattern recognition

Feature selection is a very important process in a pattern recognition system. In the paper, a GA-based fuzzy feature evaluation algorithm (GAFEA) is proposed. The relative importance of a feature element in the feature space is explored in a 2-D example, and a weighting factor is assigned to each feature element to magnify or reduce its importance in the feature space. In addition, a fuzzy feature evaluation index (FFEI) is defined for the measurement of ambiguity of intraclass and interclass. By integrating the weighting factors with the index FFEI, the index FFEI becomes a function of the weighting set. Based on minimizing FFEI, the degrees of the relative importance of features in the multi-dimensional feature space can be found by the genetic algorithm. Finally, an experiment is conducted to justify the validity of the proposed GAFEA. The results show that the task of the feature evaluation can be achieved by the proposed GAFEA before selecting the optimal subset of features.