MLKD logo   Machine Learning &
Knowledge Discovery Group

Publication Details

  Author(s): G. Tsoumakas, I. Vlahavas.

Title: “An interoperable and scalable Web-based system for classifier sharing and fusion”.

Availability: Click here to download the PDF (Acrobat Reader) file (17 pages).


Appeared in: Expert Systems with Applications, Elsevier, 33(3), pp. 716-724, 2007.

Abstract: This paper describes CSF/DC, a Web-based system for classifier sharing and fusion. CSF/DC enables the sharing of classification models, by allowing the upload and download of such models expressed in the industry standard PMML language on the system’s online classifier repository. CSF/DC also leverages the individual knowledge shared by such (potentially heterogeneous) classification models and offers quality decision support to any user with an Internet connection through a guided procedure. However, some organizations or individuals might want to share the predictive capabilities of their classification models without compromising their internal structure. This is accommodated by CSF/DC through the use of Web services. Specifically, CSF/DC allows the participation of classifier Web services in the decision fusion process, by offering the necessary online mechanisms for the registration and invocation of such Web services developed and installed at remote sites.

Relevant Links: