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Evaluating the use of different measure functions in the predictive quality of ABC-miner

Published: 06 July 2013 Publication History

Abstract

Learning classifiers from datasets is a central problem in data mining and machine learning research. ABC-Miner is an Ant-based Bayesian Classification algorithm that employs the Ant Colony Optimization (ACO) meta-heuristics to learn the structure of Bayesian Augmented Naïve-Bayes (BAN) Classifiers. One of the most important aspects of the ACO algorithm is the choice of the quality measure used to evaluate a candidate solution to update pheromone. In this paper, we explore the use of various classification quality measures for evaluating the BAN classifiers constructed by the ants. The aim is to discover how the use of different evaluation measures affects the quality of the output classifier in terms of predictive accuracy. In our experiments, we use 4 different classification measures on 15 benchmark datasets.

References

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UCI Repository of Machine Learning Databases. Retrieved Oct 2011 from, URL: www.ics.uci.edu/mlearn/MLRepository.html.
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M. Dorigo and T. Stützle. Ant Colony Optimization. The MIT Press, 2004.
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J. Furnkranz and P. Flach. ROC 'n Rule Learning - Towards a Better Understanding of Covering Algorithms. Machine Learning, pages 39--77, 2005.
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F. Janssen and J. Furnkranz. On the quest for optimal rule learning heuristics. Machine Learning, pages 343--379, 2010.
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M. Medland and F. Otero. A Study of Different Quality Evaluation Functions in the cAnt-Miner (PB) Classification Algorithm. 4th International Conference on Genetic and Evolutionary Computation Confherence (GECCO 2012), pages 49--56, 2012.
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K. M. Salama and A. M. Abdelbar. Exploring Different Rule Quality Evaluation Functions in ACO-based Classification Algorithms. IEEE Swarm Intelligence Symposium, pages 1--8, 2011.
[7]
K. M. Salama and A. A. Freitas. ABC-Miner: an Ant-based Bayesian Classification Algorithm. International Conference on Swarm Intelligence (ANTS), pages 2677--2694, 2012.
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I. H. Witten and E. Frank. Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, 3rd edition, 2010.

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    cover image ACM Conferences
    GECCO '13 Companion: Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
    July 2013
    1798 pages
    ISBN:9781450319645
    DOI:10.1145/2464576
    • Editor:
    • Christian Blum,
    • General Chair:
    • Enrique Alba
    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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    New York, NY, United States

    Publication History

    Published: 06 July 2013

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    Author Tags

    1. ant colony optimization
    2. bayesian network classifiers
    3. classification
    4. data mining

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    GECCO '13
    Sponsor:
    GECCO '13: Genetic and Evolutionary Computation Conference
    July 6 - 10, 2013
    Amsterdam, The Netherlands

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