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Optimization of Decision Rules Based on Dynamic Programming Approach

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Innovations in Intelligent Machines-4

Part of the book series: Studies in Computational Intelligence ((SCI,volume 514))

Abstract

This chapter is devoted to the study of an extension of dynamic programming approach which allows optimization of approximate decision rules relative to the length and coverage. We introduce an uncertainty measure that is the difference between number of rows in a given decision table and the number of rows labeled with the most common decision for this table divided by the number of rows in the decision table. We fix a threshold γ, such that 0 ≤ γ < 1, and study so-called γ-decision rules (approximate decision rules) that localize rows in subtables which uncertainty is at most γ. Presented algorithm constructs a directed acyclic graph Δγ T which nodes are subtables of the decision table T given by pairs “attribute = value”. The algorithm finishes the partitioning of a subtable when its uncertainty is at most γ. The chapter contains also results of experiments with decision tables from UCI Machine Learning Repository.

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Authors and Affiliations

  1. Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal, 23955-6900, Saudi Arabia

    Beata Zielosko, Igor Chikalov, Mikhail Moshkov & Talha Amin

  2. Institute of Computer Science, University of Silesia, 39, Będzińska St, 41-200, Sosnowiec, Poland

    Beata Zielosko

Authors
  1. Beata Zielosko
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  2. Igor Chikalov
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  3. Mikhail Moshkov
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  4. Talha Amin
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Corresponding author

Correspondence to Beata Zielosko .

Editor information

Editors and Affiliations

  1. Ecole Polytechnique Universitaire de Marseille, Aix-Marseille University (AMU), Marseille, France

    Colette Faucher

  2. Faculty of Education, Science, Technology & Mathematics, University of Canberra, Canberra, Australia

    Lakhmi C. Jain

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Zielosko, B., Chikalov, I., Moshkov, M., Amin, T. (2014). Optimization of Decision Rules Based on Dynamic Programming Approach. In: Faucher, C., Jain, L. (eds) Innovations in Intelligent Machines-4. Studies in Computational Intelligence, vol 514. Springer, Cham. https://doi.org/10.1007/978-3-319-01866-9_12

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  • DOI: https://doi.org/10.1007/978-3-319-01866-9_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-01865-2

  • Online ISBN: 978-3-319-01866-9

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