An Empirical Comparison of Classifiers for Multi-Class Imbalance Learning

This paper presents empirical analysis of binarisation solutions to multi-class problems such as one-vs-one (OVO), one-vs-rest (OVR), error correcting output code (ECOC) that make use of Linear SVM as a base learner and other ensemble-based solutions like Bagging, Random Forest with decision tree as the base learner. The data sets used for experiments are multi-class and imbalanced in nature and have been imported from UCI machine learning repository. Due to the imbalanced nature of datasets, we have evaluated all the multi-class algorithm using the performance metrics such as F1-Score, Precision, Recall and Gmean. And then we compared all the algorithms over all the benchmark datasets using Friedman's statistical test. We have also analysed the impact of non-intelligent sampling techniques on these algorithms. Sci-Kit, imbalanced-learn machine learning tool kits were used to carry out the experiments in this paper.