An interactive feature selection method based on multi-step state transition algorithm for high-dimensional data

Feature selection (FS) has been extensively employed in classification tasks to reduce data dimensionality and enhance prediction performance effectively. Recently, hybrid filter-wrapper methods have exhibited promising results in FS problems by leveraging both advantages. However, the inadequate integration of the filter method into the wrapper method leads to the hybrid algorithms exhibiting poor efficiency in classification as datasets grow in complexity. In this paper, an interactive feature selection framework based on state transition algorithm (STA) is proposed to address high-dimensional FS problems. In this framework, prior knowledge of the features is formed via the mutual information-based filter method. The STA is a powerful search engine that traverses the feature space in the wrapper stage. Moreover, an external trainer with prior knowledge will guide the exploration direction of STA in a simple but efficient way to accelerate the search process. And a self-adaptive mechanism is proposed to adjust the prior knowledge during the search process. Specifically, the external trainer establishes the interactive loop, while the self-adaptive mechanism aims to feed feature information back to the loop. In addition, the search process is prevented from being trapped in local optima by employing a multi-step STA, which allows for continuous transformations of the solution in probability. Finally, the proposed FS method is applied to various public classification datasets. The experimental results demonstrate that the proposed method is a highly competitive FS method, outperforming several state-of-the-art algorithms in generating an optimal subset of features.

Automated summary

In this paper, an interactive feature selection framework based on state transition algorithm is proposed to address high-dimensional feature selection problems. The framework combines the advantages of filter and wrapper methods to improve classification efficiency, and employs self-adaptive mechanism and multi-step STA to avoid local optima.