Region Purity-Based Local Feature Selection : A Multiobjective Perspective


*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

8 Citations (Scopus)


In contrast to the traditional feature selection (FS), local FS (LFS) partitions the whole sample space and obtains the feature subset for each local region. However, most existing LFS algorithms lack a problem-specific objective function and instead simply apply the distance-like objective function, which limits their classification performance. In addition, obtaining a good LFS model is essentially a multiobjective optimization problem. Therefore, in this article, we propose a region purity (RP)-based LFS (RP-LFS) where, besides the proportion of the selected features and region-based distance metric, we design a novel objective function, RP, from the perspective of combining local features with classifiers. To solve the RP-LFS, an improved nondominated sorting genetic algorithm III is proposed. Specifically, a network-inspired crossover operator and a quick bit mutation are applied, which can improve the ability to search for better solutions. A regional feature sharing strategy between different local models is developed, which can preserve more effective features. Experimental studies on 11 UCI datasets and nine high-dimensional datasets validate the effectiveness of our proposed RP. In comparison with various state-of-the-art FS and LFS algorithms, RP-LFS can achieve very competitive classification accuracy while obtaining a reduced feature subset size.

Original languageEnglish
Pages (from-to)787-801
Number of pages15
JournalIEEE Transactions on Evolutionary Computation
Issue number4
Early online date16 Nov 2022
Publication statusPublished - Aug 2023
Externally publishedYes

Bibliographical note

Funding Information:
This work was supported in part by the National Natural Science Foundation of China (NSFC) under Grant 61702336; in part by the Shenzhen Fundamental Research Program under Grant JCYJ2020010911041013; in part by the Hong Kong Innovation and Technology Commission (InnoHK Project CIMDA); and in part by the Hong Kong GRF-RGC General Research Fund under Grant 11209819 (CityU 9042816) and Grant 11203820 (CityU 9042598).

Publisher Copyright:
© 2022 IEEE.


  • Local feature selection (LFS)
  • multiobjective optimization
  • region purity (RP)
  • regional feature sharing strategy (RFSS)


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