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A cost-sensitive decision tree learning algorithm based on a multi-armed bandit framework

Lomax, Susan and Vadera, S 2016, 'A cost-sensitive decision tree learning algorithm based on a multi-armed bandit framework' , The Computer Journal . (In Press)

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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
Funders: University of Salford
Depositing User: S Vadera
Date Deposited: 14 Mar 2016 11:13
Last Modified: 14 Mar 2016 11:13
URI: http://usir.salford.ac.uk/id/eprint/38118

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