A fuzzy method for discovering cost-effective actions from data

Kalanat, N, Shamsinejadbabaki, P and Saraee, MH ORCID: https://orcid.org/0000-0002-3283-1912 2014, 'A fuzzy method for discovering cost-effective actions from data' , Journal of Intelligent and Fuzzy Systems, 28 (2) , pp. 757-765.

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Data mining techniques are often confined to the delivery of frequent patterns and stop short of suggesting how to act on these patterns for business decision-making. They require human experts to post-process the discovered patterns manually. Therefore a significant need exists for techniques and tools with the ability to assist users in analyzing a large number of patterns to find usable knowledge. Action mining is one of these techniques which intelligently and automatically suggests some changes in the state of an object with the aim of gaining some profit in the corresponding domain. Up to now little research has been done in this field; in all cases continuous-valued data is handled by discretizing the associated attributes in advance or during the learning process. One inherent disadvantage in these methods is that using this sharp behavior can result in missing the optimal action. To overcome this problem this paper presents a method based on fuzzy set theory. In this paper, we concentrate on the fuzzy set based approach for the enhancement of Yang's method and present an algorithm that suggests actions which will decrease the degree to which a certain object belongs to an undesired status and increase the degree to which it belongs to a desired one. Our algorithm takes into account the fuzzy cost of actions, and further, it attempts to maximize the fuzzy net profit. The contribution of the work is in taking the output from fuzzy decision trees, and producing novel, actionable knowledge through automatic fuzzy post-processing. The performance of the proposed algorithm is compared with Yang's method using several real-life datasets taken from the UCI Machine Learning Repository. Experimental results show that the proposed algorithm outperforms Yang's method not only in finding more actions but also in finding actions with more fuzzy net profit.

Item Type: Article
Themes: Media, Digital Technology and the Creative Economy
Schools: Schools > School of Computing, Science and Engineering > Salford Innovation Research Centre
Journal or Publication Title: Journal of Intelligent and Fuzzy Systems
Publisher: IOS Press
Refereed: Yes
ISSN: 1064-1246
Related URLs:
Funders: Non funded research
Depositing User: Prof. Mo Saraee
Date Deposited: 30 Jan 2015 13:24
Last Modified: 15 Feb 2022 19:02
URI: http://usir.salford.ac.uk/id/eprint/33613

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