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Ant Colony Decision Trees – A New Method for Constructing Decision Trees Based on Ant Colony Optimization

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Computational Collective Intelligence. Technologies and Applications (ICCCI 2010)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6421))

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Abstract

In this paper, we would like to propose a new method for constructing decision trees based on Ant Colony Optimization (ACO). The ACO is a metaheuristic inspired by the behavior of real ants, where they search for optimal solutions by considering both local heuristic and previous knowledge, observed by pheromone changes. Good results of the ant colony algorithms for solving combinatorial optimization problems suggest an appropriate effectiveness of the approach also in the task of constructing decision trees. In order to improve the accuracy of decision trees we propose an Ant Colony algorithm for constructing Decision Trees (ACDT). A heuristic function used in a new algorithm is based on the splitting rule of the CART algorithm (Classification and Regression Trees). The proposed algorithm is evaluated on a number of well-known benchmark data sets from the UCI Machine Learning repository. What deserves particular attention is the fact that empirical results clearly show that ACDT performs very good while comparing to other techniques.

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Author information

Authors and Affiliations

  1. Institute of Computer Science, University of Silesia, Bȩdzińska 39, 41–200, Sosnowiec, Poland

    Urszula Boryczka & Jan Kozak

Authors
  1. Urszula Boryczka
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  2. Jan Kozak
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Editor information

Editors and Affiliations

  1. Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, 415 Chien-Kung Road, 807, Kaohsiung, Taiwan

    Jeng-Shyang Pan

  2. Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, #43, Sec.4, Keelung Rd., 106, Taipei, Taiwan

    Shyi-Ming Chen

  3. Institute of Informatics, Wroclaw University of Technology, Str. Wyb. Wyspianskiego 27, 50-370, Wroclaw, Poland

    Ngoc Thanh Nguyen

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Boryczka, U., Kozak, J. (2010). Ant Colony Decision Trees – A New Method for Constructing Decision Trees Based on Ant Colony Optimization. In: Pan, JS., Chen, SM., Nguyen, N.T. (eds) Computational Collective Intelligence. Technologies and Applications. ICCCI 2010. Lecture Notes in Computer Science(), vol 6421. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16693-8_39

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  • DOI: https://doi.org/10.1007/978-3-642-16693-8_39

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-16692-1

  • Online ISBN: 978-3-642-16693-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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