Evolutionary Sequential Transfer Learning for Multi-Objective Feature Selection in Classification

Over the past decades, evolutionary multi-objective algorithms have proven their efficacy in feature selection. Nevertheless, a prevalent approach involves addressing feature selection tasks in isolation, even when these tasks share common knowledge and interdependencies. In response to this, the emerging field of evolutionary sequential transfer learning is gaining attention for feature selection. This novel approach aims to transfer and leverage knowledge gleaned by evolutionary algorithms in a source domain, applying it intelligently to enhance feature selection outcomes in a target domain. Despite its promising potential to exploit shared insights, the adoption of this transfer learning paradigm for feature selection remains surprisingly limited due to the computational expense of existing methods, which learn a mapping between the source and target search spaces. This paper introduces an advanced multi-objective feature selection approach grounded in evolutionary sequential transfer learning, strategically crafted to tackle interconnected feature selection tasks with overlapping features. Our novel framework integrates probabilistic models to capture high-order information within feature selection solutions, successfully tackling the challenges of extracting and preserving knowledge from the source domain without an expensive cost. It also provides a better way to transfer the source knowledge when the feature spaces of the source and target domains diverge. We evaluate our proposed method against four prominent single-task feature selection approaches and a cutting-edge evolutionary transfer learning feature selection method. Through empirical evaluation, our proposed approach showcases superior performance across the majority of datasets, surpassing the effectiveness of the compared methods.