A Probabilistic Based Multi-label Classification Method Using Partial Information

In recent study there exist many approaches to solve multilabel classification problems which are used in various applications such as protein function classification, music categorization, semantic scene classification, etc., It in-turn uses different evaluation metrics like hamming loss and subset loss for solving multi-label classification but which are deterministic in nature. In this paper, we concentrate on probabilistic models and develop a new probabilistic approach to solve multi-label classification. This approach is based on logistic regression. The other approach is based on the idea of grouping related labels. This method trains one classifier for each group and the corresponding label is called as group representative. Predict other labels based on the predicted labels of group representative. The relations between the labels are found using the concept of association rule mining.