Context-Dependent Fusion of Multiple Algorithms with Minimum Classification Error Learning

We present a novel method for fusing the decisions of multiple classification algorithms which use different features, classification methods, and data sources. The proposed method, called context dependent fusion of multiple algorithms (CDF-MA) is motivated by the fact that the relative performance of different algorithms can vary significantly as the characteristics of the input data vary. The training part of CDF-MA has two main components: context extraction and algorithm fusion. In context extraction, the features used by the distinct algorithms are combined and clustered into groups of similar contexts. The algorithm fusion component embeds a variation of the MCE/GPD method to assigns an aggregation weight to each algorithm based on its loss function within each context. Results on real world data show that the proposed method can identify meaningful and coherent clusters, and outperform all individual classifiers and the global weighted average fusion method.