Efficient label ordering for improving multi-label classifier chain accuracy

Classifier chain (CC) algorithms have been introduced for multi label classification predictions in recent years. The accuracy of these algorithms is considered better than the other state-of-the art algorithms in this domain. In addition to accuracy, an effort is made to improve the complexity of the algorithms in order to predict an optimal order in which the binary classifiers are executed. Existing label ordering algorithms are executed twice, once for the generation of label ordering and another time for improving classifier chain accuracy with predicted order. In this paper, we discuss the current chain classifier algorithms and their comparison in terms of both accuracy and execution time. Moreover, we have introduced a new Label Ordering for Classifier Chain (LOCC), which exploits the semantic relationships among the labels of a dataset. The predicted label’s order is computed without the execution of the classification algorithm. The semantic relations among the labels are analysed and an order is generated, which is fed to a classifier chain algorithm. The proposed algorithm is better in terms of accuracy and computational time than the available classifier chain algorithms.