By Topic

Building decision trees using functional dependencies

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Kwok-Wa Lam ; Dept. of Comput. Sci., City Univ. of Hong Kong, Kowloon, China ; Lee, V.C.S.

Decision tree (DT) induction is regarded as a representative of traditional approaches to classification rule mining which is an important technique for many data mining applications. Using a heuristic-based local search, DT induction appends attribute at a time to rules in the order of goodness. This method may eliminate some typical structures that several attributes collectively determine the class. Recently, there has been growing interest in the problem of discovering functional dependencies (FDs) from existing databases [[Flach et al.], [Y. Huhtala et al., (1999)], [Lopes et al.], [Novelli et al.]]. Some efficient and scalable algorithms have been proposed. In this paper, we present a new method to build a DT classifier using approximate FDs [Y. Huhtala et al., (1999)]. The new method is different from the traditional ways of building DTs in that it searches composite attributes for individual node of a DT which leads to substantially smaller and more understandable DTs without adversely affecting the accuracy gains. Experiments showed that the new method not only builds more accurate classifiers, but also does this with more compact structures.

Published in:

Information Technology: Coding and Computing, 2004. Proceedings. ITCC 2004. International Conference on  (Volume:2 )

Date of Conference:

5-7 April 2004