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Title: A Knowledge-based Web Information System for the Federation of Distributed Classifiers
Author(s): G. Tsoumakas, N. Bassiliades, I. Vlahavas.
Availability: Click here to download the PDF (Acrobat Reader) file (38 pages).
Appeared in: Web Information Systems, D. Taniar and W. Rahayu (Eds.), Idea-Group Publishing, Chapter 8, pp. 271-308, 2004.
Abstract: This chapter presents the design and development of WebDisC, a knowledge-based Web information system for the fusion of classifiers induced at geographically distributed databases. The main features of our system are: i) a declarative rule language for classifier selection that allows the combination of syntactically heterogeneous distributed classifiers, ii) a variety of standard methods for fusing the output of distributed classifiers, iii) a new approach for clustering classifiers in order to deal with the semantic heterogeneity of distributed classifiers, detect their interesting similarities and differences and enhance their fusion and iv) an architecture based on the Web services paradigm that utilizes the open and scalable standards of XML and SOAP.
See also : WebDisC

        This paper has been cited by the following:

1 Buitendag, A. (2005) A Knowledge Support portlet framework for rural e-Agricultural communities, MSc Thesis, Tshwane University of Technology, October 2005.
2 J. Secretan, "An Architecture for High-Performance Privacy-Preserving and Distributed Data Mining”, PhD thesis, School of Electrical Engineering and Computer Science, University of Central Florida, Orlando, USA, Fall 2009.
3 Jimmy Secretan, Michael Georgiopoulos, Anna Koufakou, Kel Cardona, APHID: An architecture for private, high-performance integrated data mining, Future Generation Computer Systems, 26 (7), pp. 891-904, 2010.
4 Prusiewicz Agnieszka, Maciej Zieba, "The Proposal of Service Oriented Data Mining System for Solving Real-Life Classification and Regression Problems", Technological Innovation for Sustainability, IFIP Advances in Information and Communication Technology, Springer, Vol. 349, pp. 83-90, 2011.
5 Zięba, Maciej, “Ensemble SVMs for imbalanced data”, PhD thesis, Politechnika Wrocławska, Wydział Informatyki i Zarządzania, Wrocław, Poland, 2013.