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An empirical comparison of cost-sensitive decision tree induction algorithms

Lomax, S and Vadera, S 2011, 'An empirical comparison of cost-sensitive decision tree induction algorithms' , Expert Systems: The International Journal of Knowledge Engineering and Neural Networks, 28 (3) , pp. 227-268.

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Abstract

Decision tree induction is a widely used technique for learning from data which first emerged in the 1980s. In recent years, several authors have noted that in practice, accuracy alone is not adequate, and it has become increasingly important to take into consideration the cost of misclassifying the data. Several authors have developed techniques to induce cost-sensitive decision trees. There are many studies that include pair-wise comparisons of algorithms, but the comparison including many methods has not been conducted in earlier work. This paper aims to remedy this situation by investigating different cost-sensitive decision tree induction algorithms. A survey has identified 30 cost-sensitive decision tree algorithms, which can be organized into ten categories. A representative sample of these algorithms has been implemented and an empirical evaluation has been carried. In addition, an accuracy based look-ahead algorithm has been extended to a new cost-sensitive look-ahead algorithm and also evaluated. The main outcome of the evaluation is that an algorithm based on genetic algorithms, known as ICET, performed better over all the range of experiments thus showing that to make a decision tree cost-sensitive, it is better to include all the different types of costs i.e., cost of obtaining the data and misclassification costs, in the induction of the decision tree.

Item Type: Article
Uncontrolled Keywords: Data mining, cost-sensitive learning, decision trees
Themes: Media, Digital Technology and the Creative Economy
Subjects outside of the University Themes
Schools: Colleges and Schools > College of Science & Technology
Colleges and Schools > College of Science & Technology > School of Computing, Science and Engineering
Colleges and Schools > College of Science & Technology > School of Computing, Science and Engineering > Data Mining and Pattern Recognition Research Centre
Journal or Publication Title: Expert Systems: The International Journal of Knowledge Engineering and Neural Networks
Publisher: Blackwell Publishing
Refereed: Yes
ISSN: 1468-0394
Related URLs:
Depositing User: S Vadera
Date Deposited: 27 Jul 2011 09:45
Last Modified: 18 Aug 2014 14:10
URI: http://usir.eprints.org/id/eprint/16861

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