Evaluation of Resampling Methods in the Class Unbalance Problem

Kubus Mariusz ()
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Kubus Mariusz: Opole University of Technology, Opole, Poland

Econometrics. Advances in Applied Data Analysis, 2020, vol. 24, issue 1, 39-50

Abstract: The purpose of many real world applications is the prediction of rare events, and the training sets are then highly unbalanced. In this case, the classifiers are biased towards the correct prediction of the majority class and they misclassify a minority class, whereas rare events are of the greater interest. To handle this problem, numerous techniques were proposed that balance the data or modify the learning algorithms. The goal of this paper is a comparison of simple random balancing methods with more sophisticated resampling methods that appeared in the literature and are available in R program. Additionally, the authors ask whether learning on the original dataset and using a shifted threshold for classification is not more competitive. The authors provide a survey from the perspective of regularized logistic regression and random forests. The results show that combining random under-sampling with random forests has an advantage over other techniques while logistic regression can be competitive in the case of highly unbalanced data.

Keywords: class unbalance; resampling; regularized logistic regression; random forests (search for similar items in EconPapers)
JEL-codes: C1 C38 C52 (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:eaiada:v:24:y:2020:i:1:p:39-50:n:4

DOI: 10.15611/eada.2020.1.04

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