A label ranking method based on Gaussian mixture model

Label ranking studies the issue of learning a model that maps instances to rankings over a finite set of predefined labels. In order to relieve the cost of memory and time during training and prediction, we propose a novel approach for label ranking problem based on Gaussian mixture model in this paper. The key idea of the approach is to divide the label ranking training data into multiple clusters using clustering algorithm, and each cluster is described by a Gaussian prototype. Then, a Gaussian mixture model is introduced to model the mapping from instances to rankings. Finally, a predicted ranking is obtained with maximum posterior probability. In the experiments, we compare our method with two state-of-the-art label ranking approaches. Experimental results show that our method is fully competitive in terms of predictive accuracy. Moreover, the proposed method also provides a measure of the reliability of the corresponding predicted ranking.