Cost-sensitive Hierarchical Clustering for Dynamic Classifier Selection

Given an ensemble of classifiers, dynamic classifier selection (DCS) selects one classifier depending on the particular input vector that we get to classify. DCS is a special case of algorithm selection (AS) where we can choose from multiple different algorithms to process a given input. We investigate if cost-sensitive hierarchical clustering (CSHC), a method originally developed for AS, is suited for DCS. We tailor CSHC for the special case of choosing a classification algorithm and compare with state-of-the-art DCS methods. We then show how the new methodology can be used for stacking. Experimental results show that CSHC-based DCS outperforms the best methods to date.