Comparative Analysis of Multi-label Classification Algorithms

Abstract

Multi-label classification has generated enthusiasm in many fields over the last few years. It allows the classifications of dataset where each instance can be associated with one or more label. It has successfully ended up being superiorstrategy as compared to Single labelclassification. In this paper, we provide an overview of multi-label classification approaches. We also discussed the various tools thatutilizes MLC approaches. Lastly, we have presented an experimental study to compare different algorithms of multi-label classification. After applying and studying the accuracies of various multilabel classification techniques, we have found that performance of Random Forest is better than the rest of the other compared multilabelclassification algorithms with 96% accuracy.