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Classifying Continuous Classes with Reinforcement Learning RULES

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Book cover Intelligent Information and Database Systems (ACIIDS 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9012))

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Abstract

Autonomous machines are interesting for both researchers and regular people. Everyone wants to have a self control machine that do the work by itself and deal with all types of problems. Thus, supervised learning and classification became important for high-dimensional and complex problems. However, classification algorithms only deals with discrete classes while practical and real-life applications contain continuous labels. Although several statistical techniques in machine learning were applied to solve this problem but they act as a black box and their actions are difficult to justify. Covering algorithms (CA), however, is one type of inductive learning that can be used to build a simple and powerful repository. Nevertheless, current CA approaches that deal with continuous classes are bias, non-updatable, overspecialized and sensitive to noise, or time consuming. Consequently, this paper proposes a novel non-discretization algorithm that deal with numeric classes while predicting discrete actions. It is a new version of RULES family called RULES-3C that learns interactively and transfer experience through exploiting the properties of reinforcement learning. This paper will investigate and assess the performance of RULES-3C with different practical cases and algorithms. Friedman test is also applied to rank RULES-3C performance and measure its significance.

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Author information

Authors and Affiliations

  1. Information Technology Department, College of Computer and Information Sciences, King Saud University, Riyadh, 11415, Saudi Arabia

    Hebah ElGibreen

  2. Information System Department, College of Computer and Information Sciences, King Saud University, Riyadh, 11543, Saudi Arabia

    Mehmet Sabih Aksoy

Authors
  1. Hebah ElGibreen
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  2. Mehmet Sabih Aksoy
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Corresponding author

Correspondence to Hebah ElGibreen .

Editor information

Editors and Affiliations

  1. Ton Duc Thang University, Ho Chi Minh city, Vietnam

    Ngoc Thanh Nguyen

  2. Wroclaw University of Technology, Wroclaw, Poland

    Bogdan Trawiński

  3. Bina Nusantara University, Jakarta, Indonesia

    Raymond Kosala

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ElGibreen, H., Aksoy, M.S. (2015). Classifying Continuous Classes with Reinforcement Learning RULES. In: Nguyen, N., Trawiński, B., Kosala, R. (eds) Intelligent Information and Database Systems. ACIIDS 2015. Lecture Notes in Computer Science(), vol 9012. Springer, Cham. https://doi.org/10.1007/978-3-319-15705-4_12

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  • DOI: https://doi.org/10.1007/978-3-319-15705-4_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-15704-7

  • Online ISBN: 978-3-319-15705-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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