An empirical comparison of cost-sensitive decision tree induction algorithms

Lomax, S; Vadera, S

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.

Citation

Lomax, S., & Vadera, S. (2011). An empirical comparison of cost-sensitive decision tree induction algorithms. Expert Systems, 28(3), 227-268. https://doi.org/10.1111/j.1468-0394.2010.00573.x

• Journal Article Type: Article
• Publication Date: Jul 1, 2011
• Deposit Date: Jul 27, 2011
• Journal: Expert Systems: The International Journal of Knowledge Engineering and Neural Networks
• Print ISSN: 0266-4720
• Publisher: Wiley
• Peer Reviewed: Peer Reviewed
• Volume: 28
• Issue: 3
• Pages: 227-268
• DOI: https://doi.org/10.1111/j.1468-0394.2010.00573.x
• Keywords: Data mining, cost-sensitive learning, decision trees
• Publisher URL: http://dx.doi.org/10.1111/j.1468-0394.2010.00573.x
• Related Public URLs: http://www.mendeley.com/profiles/sunil-vadera/