Skip to the content

Inducing safer oblique trees without costs

Vadera, S 2005, 'Inducing safer oblique trees without costs' , Expert Systems, 22 (4) , pp. 206-221.

[img] PDF (Publisher's version)
Restricted to Repository staff only

Download (299kB)
    [img]
    Preview
    PDF - Accepted Version
    Download (257kB) | Preview

      Abstract

      Decision tree induction has been widely studied and applied. In safety applications, such as determining whether a chemical process is safe or whether a person has a medical condition, the cost of misclassification in one of the classes is significantly higher than in the other class. Several authors have tackled this problem by developing cost-sensitive decision tree learning algorithms or have suggested ways of changing the distribution of training examples to bias the decision tree learning process so as to take account of costs. A prerequisite for applying such algorithms is the availability of costs of misclassification. Although this may be possible for some applications, obtaining reasonable estimates of costs of misclassification is not easy in the area of safety. This paper presents a new algorithm for applications where the cost of misclassifications cannot be quantified, although the cost of misclassification in one class is known to be significantly higher than in another class. The algorithm utilizes linear discriminant analysis to identify oblique relationships between continuous attributes and then carries out an appropriate modification to ensure that the resulting tree errs on the side of safety. The algorithm is evaluated with respect to one of the best known cost-sensitive algorithms (ICET), a well-known oblique decision tree algorithm (OC1) and an algorithm that utilizes robust linear programming.

      Item Type: Article
      Themes: Subjects / Themes > Q Science > QA Mathematics > QA075 Electronic computers. Computer science
      Subjects / Themes > Q Science > QA Mathematics
      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
      Publisher: Wiley Blackwell Publishing
      Refereed: Yes
      ISSN: 02664720
      Depositing User: H Kenna
      Date Deposited: 07 Jan 2009 14:44
      Last Modified: 20 Aug 2013 16:51
      URI: http://usir.salford.ac.uk/id/eprint/944

      Document Downloads

      More statistics for this item...

      Actions (login required)

      Edit record (repository staff only)