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Proceedings Paper

Dynamic integration of multiple data mining techniques in a knowledge discovery management system
Author(s): Seppo Jumani Puuronen; Vagan Terziyan; Artyom Katasonov; Alexey Tsymbal
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Paper Abstract

One of the most important directions in improvement of data mining and knowledge discovery, is the integration of multiple classification techniques of an ensemble of classifiers. An integration technique should be able to estimate and select the most appropriate component classifiers from the ensemble. We present two variations of an advanced dynamic integration technique with two distance metrics. The technique is one variation of the stacked generalization method, with an assumption that each of the component classifiers is the best one, inside a certain sub area of the entire domain area. Our technique includes two phases: the learning phase and the application phase. During the learning phase, a performance matrix of each component classifier is derived, using the instances of the training set. Each matrix thus includes a way information concerning the 'competence area' of the corresponding component classifier. These matrixes are used during the application phase to predict the performance of each component classifier in each new instance. The technique is evaluated on three data sets, taken from the UCI machine learning repository, with which well-known classification methods have not proved successful. The comparison results show that our dynamic integration technique outperforms weighted voting and cross-validation majority techniques in some datasets.

Paper Details

Date Published: 25 February 1999
PDF: 12 pages
Proc. SPIE 3695, Data Mining and Knowledge Discovery: Theory, Tools, and Technology, (25 February 1999); doi: 10.1117/12.339975
Show Author Affiliations
Seppo Jumani Puuronen, Univ. of Jyvaskyla (Finland)
Vagan Terziyan, Kharkov State Technical Univ. of Radioelectronics (Ukraine)
Artyom Katasonov, Kharkov State Technical Univ. of Radioelectronics (Ukraine)
Alexey Tsymbal, Kharkov State Technical Univ. of Radioelectronics (Ukraine)

Published in SPIE Proceedings Vol. 3695:
Data Mining and Knowledge Discovery: Theory, Tools, and Technology
Belur V. Dasarathy, Editor(s)

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