Cost-sensitive Hierarchical Clustering for Dynamic Classifier Selection

• URL: http://arxiv.org/abs/2012.09608v2
• Date: Fri, 18 Dec 2020 16:56:07 GMT
• Title: Cost-sensitive Hierarchical Clustering for Dynamic Classifier Selection
• Authors: Meinolf Sellmann and Tapan Shah
• Abstract summary: We investigate if a method developed for general algorithm selection named cost-sensitive hierarchical clustering is suited for dynamic classifier selection. Our experimental results show that our modified CSHC algorithm compares favorably.
• Score: 3.42658286826597
• License: http://creativecommons.org/licenses/by/4.0/
• 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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