IDEAS home Printed from https://ideas.repec.org/p/pur/prukra/1264.html
   My bibliography  Save this paper

Cost-Sensitive Decision Trees with Completion Time Requirements

Author

Listed:
  • Hung-Pin KAO
  • Kwei TANG
  • Jen TANG

Abstract

In many classification tasks, managing costs and completion times are the main concerns. In this paper, we assume that the completion time for classifying an instance is determined by its class label, and that a late penalty cost is incurred if the deadline is not met. This time requirement enriches the classification problem but posts a challenge to developing a solution algorithm. We propose an innovative approach for the decision tree induction, which produces multiple candidate trees by allowing more than one splitting attribute at each node. The user can specify the maximum number of candidate trees to control the computational efforts required to produce the final solution. In the tree-induction process, an allocation scheme is used to dynamically distribute the given number of candidate trees to splitting attributes according to their estimated contributions to cost reduction. The algorithm finds the final tree by backtracking. An extensive experiment shows that the algorithm outperforms the top-down heuristic and can effectively obtain the optimal or near-optimal decision trees without an excessive computation time.

Suggested Citation

  • Hung-Pin KAO & Kwei TANG & Jen TANG, 2010. "Cost-Sensitive Decision Trees with Completion Time Requirements," Purdue University Economics Working Papers 1264, Purdue University, Department of Economics.
  • Handle: RePEc:pur:prukra:1264
    as

    Download full text from publisher

    File URL: https://business.purdue.edu/research/Working-papers-series/2010/1264.pdf
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Hung-Pin Kao & Kwei Tang, 2014. "Cost-Sensitive Decision Tree Induction with Label-Dependent Late Constraints," INFORMS Journal on Computing, INFORMS, vol. 26(2), pages 238-252, May.

    More about this item

    Keywords

    classification; decision tree; cost and time sensitive learning; late penalty;
    All these keywords.

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pur:prukra:1264. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Business PHD (email available below). General contact details of provider: https://edirc.repec.org/data/kspurus.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.