Integrating Specialized Classifiers Based on Continuous Time Markov Chain
This addresses a practical issue in real-world recognition systems where specialized classifiers are common, offering a more robust integration method, though it is incremental as it builds on ensemble techniques.
The paper tackles the problem of integrating specialized classifiers that cover different subsets of classes, which can lead to misleading predictions with existing methods like weighted average. It proposes a novel approach using a continuous-time Markov chain to combine predictions via pairwise preferences, achieving considerable improvement on large public datasets, especially with unbalanced classifier coverage.
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.