A critical review of multi-objective optimization in data mining: a position paper

Freitas, Alex A. (2004) A critical review of multi-objective optimization in data mining: a position paper. SIGKDD Explorations, 6 (2). pp. 77-86. ISSN 1931-0145. (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

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This paper addresses the problem of how to evaluate the quality of a model built from the data in a multi-objective optimization scenario, where two or more quality criteria must be simultaneously optimized. A typical example is a scenario where one wants to maximize both the accuracy and the simplicity of a classification model or a candidate attribute subset in attribute selection. One reviews three very different approaches to cope with this problem, namely: (a) transforming the original multi-objective problem into a single-objective problem by using a weighted formula; (b) the lexicographical approach, where the objectives are ranked in order of priority; and (c) the Pareto approach, which consists of finding as many non-dominated solutions as possible and returning the set of non-dominated solutions to the user. One also presents a critical review of the case for and against each of these approaches. The general conclusions are that the weighted formula approach -- which is by far the most used in the data mining literature -- is to a large extent an ad-hoc approach for multi-objective optimization, whereas the lexicographic and the Pareto approach are more principled approaches, and therefore deserve more attention from the data mining community.

Item Type: Article
Uncontrolled keywords: data mining, classification, multi-objective optimization
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Divisions: Faculties > Science Technology and Medical Studies > School of Computing > Applied and Interdisciplinary Informatics Group
Depositing User: Mark Wheadon
Date Deposited: 24 Nov 2008 18:01
Last Modified: 19 May 2014 16:01
Resource URI: https://kar.kent.ac.uk/id/eprint/14051 (The current URI for this page, for reference purposes)
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