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Number of Instances for Reliable Feature Ranking in a Given Problem

Bohanec Marko (), Borštnar Mirjana Kljajić () and Robnik-Šikonja Marko ()
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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

Business Systems Research, 2018, vol. 9, issue 2, 35-44

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

Keywords: machine learning; feature ranking; feature evaluation (search for similar items in EconPapers)
Date: 2018
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