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Classification with machine learning algorithms after hybrid feature selection in imbalanced data sets

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  • Meryem Pulat
  • İpek Deveci Kocakoç

Abstract

The efficacy of machine learning algorithms significantly depends on the adequacy and relevance of features in the data set. Hence, feature selection precedes the classification process. In this study, a hybrid feature selection approach, integrating filter and wrapper methods was employed. This approach not only enhances classification accuracy, surpassing the results achievable with filter methods alone, but also reduces processing time compared to exclusive reliance on wrapper methods. Results indicate a general improvement in algorithm performance with the application of the hybrid feature selection approach. The study utilized the Taiwanese Bankruptcy and Statlog (German Credit Data) datasets from the UCI Machine Learning Repository. These datasets exhibit an unbalanced distribution, necessitating data preprocessing that considers this unbalance. After acknowledging the datasets’ unbalanced nature, feature selection and subsequent classification processes were executed.

Suggested Citation

  • Meryem Pulat & İpek Deveci Kocakoç, 2024. "Classification with machine learning algorithms after hybrid feature selection in imbalanced data sets," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 34(4), pages 157-183.
    • Handle: RePEc:wut:journl:v:34:y:2024:i:4:p:157-183:id:10
    DOI: 10.37190/ord240410
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    References listed on IDEAS

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    1. Grubinger, Thomas & Zeileis, Achim & Pfeiffer, Karl-Peter, 2014. "evtree: Evolutionary Learning of Globally Optimal Classification and Regression Trees in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 61(i01).
    2. Jing Quan & Xuelian Sun, 2024. "Credit risk assessment using the factorization machine model with feature interactions," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-10, December.
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