A Technique for Advanced Dynamic Integration of Multiple Classifiers

A Technique for Advanced Dynamic Integration of Multiple Classifiers,Alexey Tsymbal,Seppo Puuronen,Vagan Terziyan

A Technique for Advanced Dynamic Integration of Multiple Classifiers   (Citations: 1)
BibTex | RIS | RefWorks Download
Currently electronic data repositories are growing quickly and contain huge amount of data from commercial, scientific, and other domain areas. Knowledge discovery in databases (KDD) is an emerging area that considers the process of finding previously unknown and potentially interesting patterns and relations in large databases. Most current KDD systems offer only isolated discovery tech- niques, and very few systems use a combination of the available discovery tech- niques. Our goal is to design an archit ecture of an integrated knowledge discovery management system (IKDMS), which enables integration of multiple discovery techniques forming a platform upon which different KDD applications can be build. In this paper our focus is on th e method evaluation/selection subsystem of an IKDMS. This subsystem is very important in any IKDMS because it helps a user to select an appropriate data mining method among the supported ones. We present and evaluate a technique for advanced dynamic integration of multiple classifiers that is based on the assumption that each classifier is the best only in- side certain sub domains of the whole a pplication domain. We have made experi- ments using three databases included in the University of California Machine Learning Repository achieving promising results either in diagnosis accuracy or in the time requirements of diagnostics or both.
Cumulative Annual
View Publication
The following links allow you to view full publications. These links are maintained by other sources not affiliated with Microsoft Academic Search.
Sort by: