Cost-sensitive Hierarchical Clustering for Dynamic Classifier Selection
This work addresses the problem of efficiently selecting the best classifier from an ensemble for a given input, which is relevant for practitioners seeking to optimize classification performance.
This paper investigates the application of cost-sensitive hierarchical clustering (CSHC) to the dynamic classifier selection (DCS) problem. The authors introduce modifications to CSHC specifically for classifier selection and demonstrate that their modified algorithm performs favorably against state-of-the-art DCS methods.
We consider the dynamic classifier selection (DCS) problem: Given an ensemble of classifiers, we are to choose which classifier to use depending on the particular input vector that we get to classify. The problem is a special case of the general algorithm selection problem where we have multiple different algorithms we can employ to process a given input. We investigate if a method developed for general algorithm selection named cost-sensitive hierarchical clustering (CSHC) is suited for DCS. We introduce some additions to the original CSHC method for the special case of choosing a classification algorithm and evaluate their impact on performance. We then compare with a number of state-of-the-art dynamic classifier selection methods. Our experimental results show that our modified CSHC algorithm compares favorably