Towards Scalable Feature Selection: An Evolutionary Multitask Algorithm Assisted by Transfer Learning Based Co-surrogate

When faced with large-instance datasets, existing feature selection methods based on evolutionary algorithms still face the challenge of high computational cost. To address this issue, this paper proposes a scalable evolutionary algorithm for feature selection on large-instance datasets, namely, transfer learning based co-surrogate assisted evolutionary multitask algorithm (cosEMT). Firstly, we tackle the feature selection on large-instance datasets via an evolutionary multitasking framework. The co-surrogate models are constructed to measure the similarity between each auxiliary task and main task, and the knowledge transfer between tasks is realized through instance-based transfer learning. Through the numerical relationship between the relative and absolute number of transferable instances, we propose a novel dynamic resource allocation strategy to make more efficient use of limited computational resources and accelerate evolutionary convergence. Meanwhile, an adaptive surrogate model update mechanism is proposed to balance the exploration and exploitation of the base optimizer embedded in the cosEMT framework. Finally, the proposed algorithm is compared with several state-of-the-art feature selection algorithms on twelve large-instance datasets. The experimental results show that the cosEMT framework can obtain significant acceleration in the convergence speed and high-quality solutions. All verify that cosEMT is a highly competitive method for feature selection on large-instance datasets.