MICQ-IPSO: An effective two-stage hybrid feature selection algorithm for high-dimensional data

Abstract

In machine learning and pattern recognition tasks, classification performance is often degraded due to the existence of irrelevant and redundant features, especially for high-dimensional data. As a data preprocessing tool, feature selection can improve classification performance while reducing the number of features. Focusing on high-dimensional data, we propose a novel two-stage hybrid feature selection method that combines the maximum information coefficient (MIC) based Q-learning algorithm and the improved particle swarm optimization (PSO) based algorithm, named as MICQ-IPSO. In the first stage, we employed an intelligent feature pre-screening operation to get a rough feature subset, which introduced MIC value as the correlation measure and automated the determination of the screening threshold by Q-learning. In the second stage, we applied an improved PSO-based method to get an optimal feature subset. During this stage, a swarm initialization strategy based on MIC correlation was used to narrow the search range and accelerate swarm convergence. To further enhance the exploitability, a deeper local search operation was performed in the search region. Moreover, a particle reset strategy was adopted to help particles jump out of the local optimal solution. Finally, we evaluated our algorithm against several state-of-the-art feature selection approaches on 17 benchmark datasets. The experimental results demonstrate the effectiveness and competitiveness of the proposed algorithm.