Lomax, S and Vadera, S 
ORCID: https://orcid.org/0000-0001-6041-2646
2017,
'A cost-sensitive decision tree learning algorithm based on a multi-armed bandit framework'
, The Computer Journal, 60 (7)
, pp. 941-956.
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Abstract
This paper develops a new algorithm for inducing cost-sensitive decision trees that is inspired by the multi-armed bandit problem, in which a player in a casino has to decide which slot machine (bandit) from a selection of slot machines is likely to pay out the most. Game Theory proposes a solution to this multi-armed bandit problem by using a process of exploration and exploitation in which reward is maximized. This paper utilizes these concepts to develop a new algorithm by viewing the rewards as a reduction in costs, and utilizing the exploration and exploitation techniques so that a compromise between decisions based on accuracy and decisions based on costs can be found. The algorithm employs the notion of lever pulls in the multi-armed bandit game to select the attributes during decision tree induction, using a look-ahead methodology to explore potential attributes and exploit the attributes which maximizes the reward. The new algorithm is evaluated on fifteen datasets and compared to six well-known algorithms J48, EG2, MetaCost, AdaCostM1, ICET and ACT. The results obtained show that the new multi-armed based algorithm can produce more cost-effective trees without compromising accuracy. The paper also includes a critical appraisal of the limitations of the new algorithm and proposes avenues for further research.
| Item Type: | Article |
|---|---|
| Schools: | Schools > School of Computing, Science and Engineering |
| Journal or Publication Title: | The Computer Journal |
| Publisher: | Oxford University Press |
| ISSN: | 0010-4620 |
| Related URLs: | |
| Funders: | University of Salford |
| Depositing User: | S Vadera |
| Date Deposited: | 14 Mar 2016 11:13 |
| Last Modified: | 15 Feb 2022 20:24 |
| URI: | http://usir.salford.ac.uk/id/eprint/38118 |
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