A particle swarm optimization based feature selection approach to transfer learning in classification

Abstract

Transfer learning aims to use acquired knowledge from existing (source) domains to improve learning performance on a different but similar (target) domains. Feature-based transfer learning builds a common feature space, which can minimize differences between source and target domains. However, most existing feature-based approaches usually build a common feature space with certain assumptions about the differences between domains. The number of common features needs to be predefined. In this work, we propose a new feature-based transfer learning method using particle swarm optimization (PSO), where a new fitness function is developed to guide PSO to automatically select a number of original features and shift source and target domains to be closer. Classification performance is used in the proposed fitness function to maintain the discriminative ability of selected features in both domains. The use of classification accuracy leads to a minimum number of model assumptions. The proposed algorithm is compared with four state-of-the-art feature-based transfer learning approaches on three well-known real-world problems. The results show that the proposed algorithm is able to extract less than half of the original features with better performance than using all features and outperforms the four benchmark semi-supervised and unsupervised algorithms. This is the first time Evolutionary Computation, especially PSO, is utilized to achieve feature selection for transfer learning.