Shapley Consensus Deep Learning for Ensemble Pruning

Abstract

This paper targets a new foundation for designing general-purpose learning systems by establishing a consensus method that facilitates self-adaptation and flexibility to deal with different learning tasks and different data distribution. We present the Shapely Consensus Deep Learning (SCDL) as a consensus method for general-purpose intelligence without the help of a domain expert. SCDL is two-level based learning process. In the first level several deep learning models have been trained for each historical observation. The Shapley Value is determined to compute the contribution of each subset of models in the training. The models are pruned according to their contribution in the learning process. In the second level the loss information of each data distribution is saved in the knowledge base. Both levels are explored to prune the models for each new observation. We present the evaluation of the generality of SCDL using different datasets with different shapes and complexities. The results reveal the effectiveness of SCDL for weakly classification. Concretely SCDL achieved 90% of AUC with less than 86% for the baseline solutions.