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arXiv · 2012.09608

Cost-sensitive Hierarchical Clustering for Dynamic Classifier Selection

Meinolf Sellmann·Tapan Shah
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Abstract

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

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Meinolf Sellmann, Tapan Shah. 2020-12-14. Cost-sensitive Hierarchical Clustering for Dynamic Classifier Selection. https://arxiv.org/abs/2012.09608

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