Efficient feature selection for domain adaptation using Mutual Information Maximization

Abstract

[Abstract]: Green AI, an emerging research field, focuses on improving the efficiency of machine learning models. In this paper, we introduce a novel and efficient method for feature selection in domain adaptation, a type of transfer learning where the source and target domains share the feature space and task but differ in their distributions. Instead of using evolutionary algorithms, a typical approach in this field, we propose the use of filter methods, which do not require an iterative search process and are less computationally expensive. Our proposed method is Mutual Information Maximization, and our experiments show that it outperforms Particle Swarm Optimization in terms of efficiency, speed, and the ability to select a reduced subset of features while achieving competitive classification accuracy results.