Efficient treatment of outliers and class imbalance for diabetes prediction.

Autor: Nnamoko N; Department of Computer Science, Edge Hill University, Ormskirk, United Kingdom. Electronic address: nnamokon@edgehill.ac.uk., Korkontzelos I; Department of Computer Science, Edge Hill University, Ormskirk, United Kingdom. Electronic address: Yannis.Korkontzelos@edgehill.ac.uk.
Jazyk: angličtina
Zdroj: Artificial intelligence in medicine [Artif Intell Med] 2020 Apr; Vol. 104, pp. 101815. Date of Electronic Publication: 2020 Feb 10.
DOI: 10.1016/j.artmed.2020.101815
Abstrakt: Learning from outliers and imbalanced data remains one of the major difficulties for machine learning classifiers. Among the numerous techniques dedicated to tackle this problem, data preprocessing solutions are known to be efficient and easy to implement. In this paper, we propose a selective data preprocessing approach that embeds knowledge of the outlier instances into artificially generated subset to achieve an even distribution. The Synthetic Minority Oversampling TEchnique (SMOTE) was used to balance the training data by introducing artificial minority instances. However, this was not before the outliers were identified and oversampled (irrespective of class). The aim is to balance the training dataset while controlling the effect of outliers. The experiments prove that such selective oversampling empowers SMOTE, ultimately leading to improved classification performance.
(Copyright © 2020 The Authors. Published by Elsevier B.V. All rights reserved.)
Databáze: MEDLINE
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