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Scalable feature subset selection for big data using parallel hybrid evolutionary algorithm based wrapper under

Yelleti Vivek1,2, Vadlamani Ravi1, P Radha Krishna2

  • 1Center of Excellence in Analytics (renamed as Center for AI and ML), Institute for Development and Research in Banking Technology, Castle Hills Road #1, Masab Tank, Hyderabad, 500057 India.

Cluster Computing
|September 15, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces parallel evolutionary algorithms for scalable feature subset selection on big data. The proposed PB-TADE algorithm demonstrated superior performance and statistical significance over existing methods.

Keywords:
Apache sparkDifferential evolutionFeature subset selectionMapReduceMultithreadingThreshold accepting

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Area of Science:

  • Computer Science
  • Machine Learning
  • Data Mining

Background:

  • Sequential wrapper-based feature subset selection (FSS) algorithms struggle with scalability and performance on large datasets.
  • Existing methods often fail to meet the demands of big data analytics.

Purpose of the Study:

  • To develop scalable and efficient parallel and distributed hybrid evolutionary algorithms (EAs) for feature subset selection on big datasets.
  • To enhance the search capabilities and prevent premature convergence in evolutionary algorithms.

Main Methods:

  • Proposed two hybrid EAs: Parallel Binary Differential Evolution and Threshold Accepting (PB-DETA) and its variant Parallel Binary Threshold Accepting and Differential Evolution (PB-TADE) using Apache Spark.
  • Parallelized state-of-the-art algorithms: adaptive DE (ADE) and permutation based DE (DE-FS PM).
  • Utilized logistic regression (LR) for fitness evaluation, specifically the area under the receiver operator characteristic curve (AUC).

Main Results:

  • PB-TADE achieved statistically significant results compared to other tested algorithms.
  • All proposed parallel algorithms demonstrated the repeatability property.
  • The parallel model achieved a speedup of 2.2-2.9x.
  • Identified feature subsets with high AUC and minimal cardinality.

Conclusions:

  • The developed parallel and distributed hybrid EAs offer a scalable solution for feature subset selection in big data scenarios.
  • PB-TADE is a highly effective algorithm for optimizing feature selection, outperforming existing methods.
  • The parallel approach significantly improves computational efficiency while maintaining accuracy.