The anatomy of a multimodal information filter

Authors:
Yi-Leh Wu;King-Shy Goh;Beitao Li;Huaxing You;Edward Y. Chang
Affiliations:
VIMA Technologies Inc., Santa Barbara, CA;University of California, Santa Barbara, CA;University of California, Santa Barbara, CA;University of California, Santa Barbara, CA;University of California, Santa Barbara, CA
Venue:
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Year:
2003

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Abstract

The proliferation of objectionable information on the Internet has reached a level of serious concern. To empower end-users with the choice of blocking undesirable and offensive websites, we propose a multimodal information filter, named MORF. In this paper, we present MORF's core components: its confidence-based classifier, a Cross-bagging ensemble scheme, and multimodal classification algorithm. Empirical studies and initial statistics collected from the MORF filters deployed at sites in the U.S. and Asia show that MORF is both efficient and effective, due to our classification methods.