Istituto di Scienza e Tecnologie dell'Informazione     
Baglioni M., Furletti B., Turini F. DrC4.5: Improving C4.5 by means of prior knowledge. In: SAC'05 - The 2005 ACM Symposium on Applied Computing (Santa Fe, NM, USA, 13-17 March 2005). Proceedings, pp. 474 - 481. ACM, 2005.
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. Copyright 2005 ACM.
URL: http://dl.acm.org/citation.cfm?id=1066787&CFID=77622922&CFTOKEN=16094627
DOI: 10.1145/1066677.1066787
Subject Algorithms
Database systems
Expert systems
Knowledge acquisition
Trees (mathematics)
H.2.8 Database applications
I.2.1 Applications and Expert Systems

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