FSPL: A Meta-Learning Approach for a Filter and Embedded Feature Selection Pipeline

Teddy Lazebnik, Avi Rosenfeld

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

There are two main approaches to tackle the challenge of finding the best filter or embedded feature selection (FS) algorithm: searching for the one best FS algorithm and creating an ensemble of all available FS algorithms. However, in practice, these two processes usually occur as part of a larger machine learning pipeline and not separately. We posit that, due to the influence of the filter FS on the embedded FS, one should aim to optimize both of them as a single FS pipeline rather than separately. We propose a meta-learning approach that automatically finds the best filter and embedded FS pipeline for a given dataset called FSPL. We demonstrate the performance of FSPL on n = 90 datasets, obtaining 0.496 accuracy for the optimal FS pipeline, revealing an improvement of up to 5.98 percent in the model's accuracy compared to the second-best meta-learning method.

Original languageEnglish
Pages (from-to)103-115
Number of pages13
JournalInternational Journal of Applied Mathematics and Computer Science
Volume33
Issue number1
DOIs
StatePublished - 1 Mar 2023
Externally publishedYes

Keywords

  • autoML
  • feature selection pipeline
  • genetic algorithm
  • meta-learning
  • no free lunch

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