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Number of Instances for Reliable Feature Ranking in a Given Problem

Author

Listed:
  • Bohanec Marko

    (Salvirt Ltd.,Ljubljana, Slovenia)

  • Borštnar Mirjana Kljajić

    (Faculty of Organizational Sciences, University of Maribor,Kranj, Slovenia)

  • Robnik-Šikonja Marko

    (Faculty of Computer and Information Science, University of Ljubljana,Ljubljana, Slovenia)

Abstract

Background: In practical use of machine learning models, users may add new features to an existing classification model, reflecting their (changed) empirical understanding of a field. New features potentially increase classification accuracy of the model or improve its interpretability. Objectives: We have introduced a guideline for determination of the sample size needed to reliably estimate the impact of a new feature. Methods/Approach: Our approach is based on the feature evaluation measure ReliefF and the bootstrap-based estimation of confidence intervals for feature ranks. Results: We test our approach using real world qualitative business-tobusiness sales forecasting data and two UCI data sets, one with missing values. The results show that new features with a high or a low rank can be detected using a relatively small number of instances, but features ranked near the border of useful features need larger samples to determine their impact. Conclusions: A combination of the feature evaluation measure ReliefF and the bootstrap-based estimation of confidence intervals can be used to reliably estimate the impact of a new feature in a given problem

Suggested Citation

  • Bohanec Marko & Borštnar Mirjana Kljajić & Robnik-Šikonja Marko, 2018. "Number of Instances for Reliable Feature Ranking in a Given Problem," Business Systems Research, Sciendo, vol. 9(2), pages 35-44, July.
    • Handle: RePEc:bit:bsrysr:v:9:y:2018:i:2:p:35-44:n:4
    DOI: 10.2478/bsrj-2018-0017
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