Handling continuous attributes in ant colony classification algorithms

Otero, F.E.B. and Freitas, A.A. and Johnson, Colin G. (2009) Handling continuous attributes in ant colony classification algorithms. In: Proc. of the 2009 IEEE Symposium on Computational Intelligence in Data Mining (CIDM 2009), Mar 30-Apr 02, 2009, Nashville, TN,. (The full text of this publication is not available from this repository)

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Abstract

Most real-world classification problems involve continuous (real-valued) attributes, as well as, nominal (discrete) attributes. The majority of Ant Colony Optimisation (ACO) classification algorithms have the limitation of only being able to cope with nominal attributes directly. Extending the approach for coping with continuous attributes presented by cAnt-Miner (Ant-Miner coping with continuous attributes), in this paper we propose two new methods for handling continuous attributes in ACO classification algorithms. The first method allows a more flexible representation of continuous attributes' intervals. The second method explores the problem of attribute interaction, which originates from the way that continuous attributes are handled in cAnt-Miner, in order to implement an improved pheromone updating method. Empirical evaluation on eight publicly available data sets shows that the proposed methods facilitate the discovery of more accurate classification models.

Item Type: Conference or workshop item (Paper)
Uncontrolled keywords: ant colony optimization, data mining, classification
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: 29 Mar 2010 12:14
Last Modified: 17 Jul 2012 14:36
Resource URI: http://kar.kent.ac.uk/id/eprint/24089 (The current URI for this page, for reference purposes)
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