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Feature clustering-Assisted feature selection with differential evolution
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AI
DOI：10.1016/j.patcog.2023.109523.png)
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Abstract
En 中文
Modern data collection technologies may produce thousands of or even more features in a single dataset. The high dimensionality of data poses a barrier to determining discriminating features due to the curse of dimensionality. Thanks to the global search ability, many population-based feature selection approaches have been proposed. However, very few studies pay attention on that a feature selection task has multiple optimal feature subsets. To search for multiple optimal feature subsets, we propose a feature clustering -assisted feature selection method. The proposed method employs the knowledge of correlation measures to group features. And, this correlation knowledge is embedded into the encoding method and the search process. A niching-based mutation operator is also used to explore the vicinity of a target individual. The aim is to find different feature subsets with very similar or the same classification performance. In ad-dition, a modification operator is proposed aiming to increase the population diversity to improve the feature selection performance. The experiments on 16 datasets show that the proposed algorithm out-performs other popular feature selection methods in terms of classification accuracy and feature subset size.(c) 2023 Elsevier Ltd. All rights reserved.
Keywords:
Differential evolution
Feature selection
Multiple optimal feature subsets
Classification
Journal
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