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.
|PDF - Accepted Version |
Restricted to Repository staff only
Download (3713kB) | Request a copy
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.
|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|
|Depositing User:||S Vadera|
|Date Deposited:||27 Jul 2011 10:45|
|Last Modified:||20 Aug 2013 18:01|
Document DownloadsMore statistics for this item...
Actions (login required)
|Edit record (repository staff only)|