Proposing a novel multi-label mapping approach for use in SVM-based multi-class classification problems
Mohammad Saremi, Mehdi Chehel Amirani · International Journal of Data Analysis Techniques and Strategies · 2023
In this study, we propose a new method called 'multi-label mapping' (MLM) for solving multi-class classification problems by mapping them into multi-label problems. The MLM method frequently selects different combinations of base classes, merges the classes, and assigns a new label to each class. Six standard datasets are selected from the UCI machine learning repository to evaluate the proposed method. Experimental results demonstrate that the MLM method reduces 50% to 96.66% of the number of required SVM classifiers and 25.76% to 72.27% of the training-testing time in comparison with the OVA and OVO methods. It also yields a better performance in terms of accuracy, precision, recall, and overfitting. Due to the need for a very low number of the SVM binary classifiers, a low training-testing time, and an acceptable prediction error, the presented method is a potential candidate for use in pattern recognition applications and multi-class classification problems.