The University of Dublin | Trinity College -- Ollscoil Átha Cliath | Coláiste na Tríonóide
Trinity's Access to Research Archive
Home :: Log In :: Submit :: Alerts ::

School of Computer Science and Statistics >
Computer Science >
Computer Science Technical Reports >

Please use this identifier to cite or link to this item:

Title: Dynamic Integration of Classifiers in the Space of Principal Components
Author: Tsymbal, Alexey
Sponsor: Science Foundation Ireland
Keywords: Computer Science
Issue Date: 2003
Publisher: Trinity College Dublin, Department of Computer Science
Citation: Tsymbal, Alexey. 'Dynamic Integration of Classifiers in the Space of Principal Components'. - Dublin, Trinity College Dublin, Department of Computer Science, TCD-CS-2003-30, 2003, pp15
Series/Report no.: Computer Science Technical Report
Abstract: Recent research has shown the integration of multiple classifiers to be one of the most important directions in machine learning and data mining. It was shown that, for an ensemble to be successful, it should consist of accurate and diverse base classifiers. However, it is also important that the integration procedure in the ensemble should properly utilize the ensemble diversity. In this paper, we present an algorithm for the dynamic integration of classifiers in the space of extracted features (FEDIC). It is based on the technique of dynamic integration, in which local accuracy estimates are calculated for each base classifier of an ensemble, in the neighborhood of a new instance to be processed. Generally, the whole space of original features is used to find the neighborhood of a new instance for local accuracy estimates in dynamic integration. In this paper, we propose to use feature extraction in order to cope with the curse of dimensionality in the dynamic integration of classifiers. We consider classical principal component analysis and two eigenvector-based supervised feature extraction methods that take into account class information. Experimental results show that, on some data sets, the use of FEDIC leads to significantly higher ensemble accuracies than the use of plain dynamic integration in the space of original features. As a rule, FEDIC outperforms plain dynamic integration on data sets, on which both dynamic integration works (it outperforms static integration), and considered feature extraction techniques are able to successfully extract relevant features.
Appears in Collections:Computer Science Technical Reports

Files in This Item:

File Description SizeFormat
TCD-CS-2003-30.pdf116.23 kBAdobe PDFView/Open

This item is protected by original copyright

Please note: There is a known bug in some browsers that causes an error when a user tries to view large pdf file within the browser window. If you receive the message "The file is damaged and could not be repaired", please try one of the solutions linked below based on the browser you are using.

Items in TARA are protected by copyright, with all rights reserved, unless otherwise indicated.


Valid XHTML 1.0! DSpace Software Copyright © 2002-2010  Duraspace - Feedback