Neural-network based regression model with prior from ranking information
Ya-Jun Qu, Bo Dai, Bao-Gang Hu · 2010
In this work, a new algorithm, which can incorporate the ranking information as prior knowledge into the regression model, is presented. Comparing with the method that treats the ranking information as hard constraints, We handle ranking reasonably by maximization of Normalized Discount Cumulative Gain (NDCG) as information retrieval (IR) evaluation measure, which is used to evaluate the performance of ranking model. In addition, an upper bound of one minus NDCG is given by weighted pairwise loss, and a connection between weighted pairwise loss and NDCG is also revealed. In this paper, RBF regression model and the pairwise shifted hinge loss and logistic loss are proposed under the suggested approach. One benefit of the proposed approach is that the weighted pairwise loss is more reasonable than the unweighted loss and all the weights are set based on the NDCG. Finally, one synthetic example shows that the method incorporated the ranking as hard constraints into regression model may cause the deteriorated results, but the good performance is shown by the proposed method. Numerical results from three existing benchmark regression problems further confirm the beneficial aspects on the proposed approach.