TAILIEUCHUNG - Báo cáo hóa học: "Research Article A Rules-Based Approach for Configuring Chains of Classifiers in Real-Time Stream Mining Systems Brian Foo and Mihaela van der Schaar"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article A Rules-Based Approach for Configuring Chains of Classifiers in Real-Time Stream Mining Systems Brian Foo and Mihaela van der Schaar | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2009 Article ID 975640 17 pages doi 2009 975640 Research Article A Rules-Based Approach for Configuring Chains of Classifiers in Real-Time Stream Mining Systems Brian Foo and Mihaela van der Schaar Department of Electrical Engineering University of California Los Angeles UCLA 66-147E Engineering IV Building 420 Westwood Plaza Los Angeles CA 90095 USA Correspondence should be addressed to Brian Foo Received 20 November 2008 Revised 8 April 2009 Accepted 9 June 2009 Recommended by Gloria Menegaz Networks of classifiers can offer improved accuracy and scalability over single classifiers by utilizing distributed processing resources and analytics. However they also pose a unique combination of challenges. First classifiers may be located across different sites that are willing to cooperate to provide services but are unwilling to reveal proprietary information about their analytics or are unable to exchange their analytics due to the high transmission overheads involved. Furthermore processing of voluminous stream data across sites often requires load shedding approaches which can lead to suboptimal classification performance. Finally real stream mining systems often exhibit dynamic behavior and thus necessitate frequent reconfiguration of classifier elements to ensure acceptable end-to-end performance and delay under resource constraints. Under such informational constraints resource constraints and unpredictable dynamics utilizing a single fixed algorithm for reconfiguring classifiers can often lead to poor performance. In this paper we propose a new optimization framework aimed at developing rules for choosing algorithms to reconfigure the classifier system under such conditions. We provide an adaptive Markov model-based solution for learning the optimal rule when stream dynamics are initially unknown. Furthermore we discuss how rules can be decomposed

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