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

Abstract

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.Index Terms-Resource constrained stream mining, coalition-based foresighted strategy, binary classifier tree.

Adaptive Multimedia Mining on Distributed Stream Processing Systems

“…It is also shown that this algorithm approaches the optimal solution asymptotically with increasing number of available operating points per classifier. While the performance of the application can be additionally improved by deploying the coalition-based foresighted strategy [6], it requires more information exchanges and computational complexity. Hence, in this paper, we focus on demonstrating how the proposed strategy can be deployed in the IBM System S processing core middleware.…”

Resource-adaptive multimedia analysis on stream mining systems

“…This factor increases with increasing cost for false alarms, and with tightening resource constraints. Alternately, in [4] we propose a distributed solution to this problem, using gametheoretic principles. In this solution, individual classifiers select their operating points to maximize a local utility function.…”