Configuring Competing Classifier Chains in Distributed Stream Mining Systems

Networks of classifiers are capturing the attention of system and algorithmic researchers because they offer improved accuracy over single model classifiers, can be distributed over a network of servers for improved scalability, and can be adapted to available system resources. In this paper, we develop algorithms to optimally configure networks (chains) of such classifiers given system processing resource constraints. We first formally define a global performance metric for classifier chains by trading off the end-to-end probabilities of detection and false alarm. We then design centralized and distributed algorithms to provide efficient and fair resource allocation among several classifier chains competing for system resources. We use the Nash bargaining solution from game theory to ensure this. We also extend our algorithms to consider arbitrary topologies of classifier chains (with shared classifiers among competing chains). We present results for both simulated and state-of-the-art classifier chains for speaker verification operating on real telephony data, discuss the convergence of our algorithms to the optimal solution, and present interesting directions for future research.