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https://doi.org/10.2478/bsrj-2018-0017

Number of Instances for Reliable Feature Ranking in a Given Problem

Marko Bohanec orcid id orcid.org/0000-0002-5295-5111 ; Salvirt Ltd., Ljubljana, Slovenia
Mirjana Kljajić Borštnar orcid id orcid.org/0000-0003-4608-9090 ; Faculty of Organizational Sciences, University of Maribor, Kranj, Slovenia
Marko Robnik-Šikonja orcid id orcid.org/0000-0002-1232-3320 ; Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia


Puni tekst: engleski pdf 528 Kb

str. 35-44

preuzimanja: 530

citiraj


Sažetak

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-to-business 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.

Ključne riječi

machine learning; feature ranking; feature evaluation

Hrčak ID:

203480

URI

https://hrcak.srce.hr/203480

Datum izdavanja:

11.7.2018.

Posjeta: 1.320 *