A Fast Feature Selection Algorithm Based on Swarm Intelligence in Acoustic Defect Detection

Feature selection plays an important role in data mining and pattern recognition. However, most existing feature selection methods suffer from stagnation in local optimal and/or high computational cost. In general, the feature selection process can be considered as a global optimization problem. The swarm intelligence algorithm can effectively find the optimal feature subset due to its global search ability. But it usually consumes very long running time when dealing with large data sets. In this paper, feature selection is transformed into a global optimization problem, which provides a fast and efficient method based on swarm intelligence algorithm. First, we propose a global optimization framework for filter-based feature selection and its mathematical model. Furthermore, to solve the feature selection problem for acoustic defect detection, we combine the shuffled frog leaping algorithm (SFLA) with an improved minimum-redundancy maximum-relevancy (ImRMR), named SFLA-ImRMR. In the experiments, a back propagation neural network is employed to evaluate the classification performance of the selected feature subset on the test sets of acoustic defect detection. The results show that SFLA-ImRMR achieved similar performance to the other algorithms within the shortest time.