Ordering of stream mining classifiers

With the rapid growth of stored and streaming multimedia, there is an increasing need for classifying, filtering and retrieving content possessing specific features/attributes of interest. Complex features in multimedia content can be scalably identified by deploying networks of binary classifiers across distributed processing infrastructures. In this paper, we focus on building optimal topologies (chains) of networked classifiers, and present algorithms for classifier ordering and configuration, to tradeoff accuracy of the feature identification versus the incurred filtering delay. We reduce the problem of classifier topology construction to the pipeline-ordering problem, and design a solution that orders classifiers based on the underlying data characteristics, system resource constraints as well as the performance and complexity characteristics of each classifier. We also determine utility bounds on the performance of this algorithm. We then extend the algorithm to dynamically configure individual classifiers jointly with the topology construction.