Cost-sensitive Hierarchical Clustering for Dynamic Classifier Selection

TL;DR

This paper explores adapting cost-sensitive hierarchical clustering for dynamic classifier selection, proposing modifications and demonstrating competitive performance against existing methods.

Contribution

The paper introduces modifications to the cost-sensitive hierarchical clustering method tailored for dynamic classifier selection, showing improved effectiveness.

Findings

Modified CSHC performs favorably compared to state-of-the-art DCS methods.

Cost-sensitive clustering effectively addresses dynamic classifier selection.

Experimental results validate the proposed approach's competitiveness.

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