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A Study of Data Pre-processing Techniques for Imbalanced Biomedical Data Classification
Biomedical data are widely accepted in developing prediction models for
identifying a specific tumor, drug discovery and classification of human
cancers. However, previous studies usually focused on different classifiers,
and overlook the class imbalance problem in real-world biomedical datasets.
There are a lack of studies on evaluation of data pre-processing techniques,
such as resampling and feature selection, on imbalanced biomedical data
learning. The relationship between data pre-processing techniques and the data
distributions has never been analysed in previous studies. This article mainly
focuses on reviewing and evaluating some popular and recently developed
resampling and feature selection methods for class imbalance learning. We
analyse the effectiveness of each technique from data distribution perspective.
Extensive experiments have been done based on five classifiers, four
performance measures, eight learning techniques across twenty real-world
datasets. Experimental results show that: (1) resampling and feature selection
techniques exhibit better performance using support vector machine (SVM)
classifier. However, resampling and Feature Selection techniques perform poorly
when using C4.5 decision tree and Linear discriminant analysis classifiers; (2)
for datasets with different distributions, techniques such as Random
undersampling and Feature Selection perform better than other data
pre-processing methods with T Location-Scale distribution when using SVM and
KNN (K-nearest neighbours) classifiers. Random oversampling outperforms other
methods on Negative Binomial distribution using Random Forest classifier with
lower level of imbalance ratio; (3) Feature Selection outperforms other data
pre-processing methods in most cases, thus, Feature Selection with SVM
classifier is the best choice for imbalanced biomedical data learning.Comment: This paper is scheduled for inclusion in V16 N3 2020, International
Journal of Bioinformatics Research and Applications (IJBRA