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DrC4.5: Improving C4.5 by means of prior knowledge

Published: 13 March 2005 Publication History

Abstract

Classification is one of the most useful techniques for extracting meaningful knowledge from databases. Classifiers, e.g. decision trees, are usually extracted from a table of records, each of which represents an example. However, quite often in real applications there is other knowledge, e.g. owned by experts of the field, that can be usefully used in conjunction with the one hidden inside the examples. As a concrete example of this kind of knowledge we consider causal dependencies among the attributes of the data records. In this paper we discuss how to use such a knowledge to improve the construction of classifiers. The causal dependencies are represented via Bayesian Causal Maps (BCMs), and our method is implemented as an adaptation of the well known C4.5 algorithm.

References

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cover image ACM Conferences
SAC '05: Proceedings of the 2005 ACM symposium on Applied computing
March 2005
1814 pages
ISBN:1581139640
DOI:10.1145/1066677
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 13 March 2005

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SAC05: The 2005 ACM Symposium on Applied Computing
March 13 - 17, 2005
New Mexico, Santa Fe

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  • (2013)What Else Can Be Extracted from Ontologies? Influence RulesSoftware and Data Technologies10.1007/978-3-642-36177-7_17(270-285)Online publication date: 2013
  • (2012)Knowledge discovery in ontologiesIntelligent Data Analysis10.5555/2608519.260852916:3(513-534)Online publication date: 1-May-2012
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