Application of preprocessing methods to imbalanced clinical data: An experimental study
Information Technologies in Medicine: 5th International Conference, ITIB 2016 …, 2016•Springer
In this paper we describe an experimental study where we analyzed data difficulty factors
encountered in imbalanced clinical data sets and examined how selected data
preprocessing methods were able to address these factors. We considered five data sets
describing various pediatric acute conditions. In all these data sets the minority class was
sparse and overlapped with the majority classes, thus difficult to learn. We studied five
different preprocessing methods: random under-and oversampling, SMOTE, neighborhood …
encountered in imbalanced clinical data sets and examined how selected data
preprocessing methods were able to address these factors. We considered five data sets
describing various pediatric acute conditions. In all these data sets the minority class was
sparse and overlapped with the majority classes, thus difficult to learn. We studied five
different preprocessing methods: random under-and oversampling, SMOTE, neighborhood …
Abstract
In this paper we describe an experimental study where we analyzed data difficulty factors encountered in imbalanced clinical data sets and examined how selected data preprocessing methods were able to address these factors. We considered five data sets describing various pediatric acute conditions. In all these data sets the minority class was sparse and overlapped with the majority classes, thus difficult to learn. We studied five different preprocessing methods: random under- and oversampling, SMOTE, neighborhood cleaning rule and SPIDER2 that were combined with the following classifiers: k-nearest neighbors, decision trees and rules, naive Bayes, neural networks and support vector machines. Application of preprocessing always improved classification performance, and the largest improvement was observed for random undersampling. Moreover, naive Bayes was the best performing classifier regardless of a used preprocessing method.
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