A framework for distributed multimedia stream mining systems using coalition-based foresighted strategies

In this paper, we propose a distributed solution to the problem of configuring classifier trees in distributed stream mining systems. The configuration involves selecting appropriate false-alarm detection tradeoffs for each classifier to minimize end-to-end penalty in terms of misclassification cost. In the proposed solution, individual classifiers select their operating points (i.e., actions) to maximize a local utility function. The utility may be purely local to the current classifier, corresponding to a myopic strategy, or may include the impact of the classifier actions on successive classifiers in the tree, corresponding to a foresighted strategy. We analytically show that actions determined by the foresighted strategies can improve the end-to-end performance of the classifier tree and derive an associated probability bound. We then evaluate our solutions on an application for hierarchical sports scene classification. By comparing centralized, myopic and foresighted solutions, we show that foresighted strategies result in better performance than myopic strategies, and also asymptotically approach the centralized optimal solution.