Integrating Specialized Classifiers Based on Continuous Time Markov Chain

TL;DR

This paper introduces a novel ensemble method that models specialized classifiers as a continuous-time Markov chain, improving prediction accuracy especially with unbalanced classifier coverage.

Contribution

It proposes a new approach that decomposes classifier predictions into pairwise preferences and uses Markov chain equilibrium for better integration.

Findings

Significant accuracy improvements on large datasets

Effective handling of unbalanced classifier coverage

Outperforms mainstream ensemble methods

Abstract

Specialized classifiers, namely those dedicated to a subset of classes, are often adopted in real-world recognition systems. However, integrating such classifiers is nontrivial. Existing methods, e.g. weighted average, usually implicitly assume that all constituents of an ensemble cover the same set of classes. Such methods can produce misleading predictions when used to combine specialized classifiers. This work explores a novel approach. Instead of combining predictions from individual classifiers directly, it first decomposes the predictions into sets of pairwise preferences, treating them as transition channels between classes, and thereon constructs a continuous-time Markov chain, and use the equilibrium distribution of this chain as the final prediction. This way allows us to form a coherent picture over all specialized predictions. On large public datasets, the proposed method obtains considerable improvement compared to mainstream ensemble methods, especially when the classifier coverage is highly unbalanced.