A Directed Inference Approach towards Multi-class Multi-model Fusion

In this paper, we propose a directed inference approach for multi-class multi-model fusion. Different from traditional approaches that learn a model in training stage and apply the model to new data points in testing stage, directed inference approach constructs (one) general direction of inference in training stage, and constructs an individual (ad-hoc) rule for each given test point in testing stage. In the present work, we propose a framework for applying the directed inference approach to multiple model fusion problems that consists of three components: (i) learning of individual models on the training samples, (ii) nearest neighbour search for constructing individual rules of bias correction, and (iii) learning of an optimal combination weights of individual models for model fusion. For inference on a test sample, the prediction scores of individual models are first corrected with bias estimated from the nearest training data points, and then the corrected scores are combined using the learned optimal weights. We conduct extensive experiments and demonstrate the effectiveness of the proposed approach towards multi-class multiple model fusion.