Multi-label Classification Using Genetic-Based Machine Learning

Multi-label classification deals with problem domains in which each instance belongs to more than one class simultaneously. Label Powerset (LP) is an efficient multi-label learning algorithm that considers each distinct combination of labels in training data as a unique new class and trains a conventional multi-class learning algorithm. In this paper a Multi-label classification algorithm is proposed that integrates LP with a rule-based evolutionary machine learning approach developed for supervised learning tasks, namely sUpervised Learning Classifiers (UCS). Moreover, to improve the prediction capability of the model on unseen instances, a prediction aggregation strategy is proposed to make efficient use of all the potentially helpful information in the rule base. The result is a multi-label rule-based evolutionary learner, which is called MLRBC (Multi-Label Rule-Based Classifier). Taking advantage of the strong generalization capability of UCS and its robustness in handling data sets with imbalanced classes, the proposed MLRBC algorithm is able to address some of the challenges involved in using LP. Experimental studies on multiple real-world datasets show that the proposed algorithm substantially improves the performance of the original LP technique and shows competitive performance against some of the state of the art multi-label learning algorithms.