ON MERGING CLASSIFICATION RULES

B. Boutsinas () and S. Athanasiadis ()
Additional contact information
B. Boutsinas: University of Patras, Department of Business Administration, University of Patras Artificial Intelligence Research Center [UPAIRC], GR-26500, RIO, Greece
S. Athanasiadis: University of Patras Artificial Intelligence Research Center [UPAIRC], GR-26500, RIO, Greece

International Journal of Information Technology & Decision Making (IJITDM), 2008, vol. 07, issue 03, 431-450

Abstract: One of the main challenges of today's data mining systems is their ability to manage a huge volume of data generated possibly by different sources. On the other hand, inductive learning algorithms have been extensively researched in machine learning using small amounts of judiciously chosen laboratory examples. There is an increasing concern in classifiers handling data that are substantially larger than available main memory on a single processor. One approach to the problem is to combine the results of different classifiers supplied with different subsets of the data, in parallel. In this paper, we present an efficient algorithm for combining partial classification rules. Moreover, the proposed algorithm can be used to match classification rules in a distributed environment, where different subsets of data may have different domains. The latter is achieved by using given concept hierarchies for the identification of matching classification rules. We also present empirical tests that demonstrate that the proposed algorithm has a significant speedup with respect to the analog non-distributed classification algorithm, at a cost of a lower classification accuracy.

Keywords: Data mining; classification; meta-learning (search for similar items in EconPapers)
Date: 2008
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.worldscientific.com/doi/abs/10.1142/S0219622008003034
Access to full text is restricted to subscribers

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wsi:ijitdm:v:07:y:2008:i:03:n:s0219622008003034

Ordering information: This journal article can be ordered from

DOI: 10.1142/S0219622008003034

Access Statistics for this article

International Journal of Information Technology & Decision Making (IJITDM) is currently edited by Yong Shi

More articles in International Journal of Information Technology & Decision Making (IJITDM) from World Scientific Publishing Co. Pte. Ltd.
Bibliographic data for series maintained by Tai Tone Lim ().