Optimal model selection for k-nearest neighbours ensemble via sub-bagging and sub-sampling with feature weighting

Naz Gul, Wali Khan Mashwani, Muhammad Aamir, Saeed Aldahmani, Zardad Khan

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

This paper proposes two novel approaches based on feature weighting and model selection for building more accurate kNN ensembles. The first approach identifies the nearest observations using a feature weighting scheme concerning the response variable via support vectors. A randomly selected subset of features is used for the feature weighting and model construction. After building a sufficiently large number of base models on bootstrap samples, a subset of the models is selected based on out-of-bag prediction error for the final ensemble. The second approach builds base learners build on random subsamples instead of bootstrap samples with a random subset of features. The method uses feature weighting while building the models. The remaining observations from each sample are used to assess the corresponding base learner and select a subset of the models for the final ensemble. The suggested ensemble methods are assessed on 12 benchmark datasets against other classical methods, including kNN-based models. The analyses reveal that the proposed methods are often better than the others.

Original languageEnglish
Pages (from-to)157-168
Number of pages12
JournalAlexandria Engineering Journal
Volume72
DOIs
Publication statusPublished - Jun 1 2023

Keywords

  • Feature weighting
  • Model selection
  • Nearest neighbours ensemble
  • Support vectors

ASJC Scopus subject areas

  • General Engineering

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