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International Conference on Machine Learning, Optimization, and Data Science

LOD 2019: Machine Learning, Optimization, and Data Science pp 180–191Cite as

The Measure of Regular Relations Recognition Applied to the Supervised Classification Task

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11943))

Abstract

The probability measure of regularities recognition in an information stream is introduced in the paper. The measure allows for the creation of machine-learning models without a supervisor. The experiment described in the paper empirically proves that the measure allows the recognition of regularities and helps to find out regular relations between the values of variables.

The machine learning model finds out regular relations in data set and by the model allow reconstructing unknown values of the classification variable. The classification algorithm on the basis of the probability measure of regularities recognition is described in the paper. The measure connection with entropy is demonstrated and mutual information is used to optimise the algorithm’s performance. The accuracy of the algorithm matches the accuracy of well-known supervised machine learning algorithms and also exceeds them.

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Author information

Authors and Affiliations

  1. Saint Petersburg State University, Saint Petersburg, Russia

    Yuriy Mikheev

Authors
  1. Yuriy Mikheev
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Corresponding author

Correspondence to Yuriy Mikheev .

Editor information

Editors and Affiliations

  1. University of Cambridge, Cambridge, UK

    Giuseppe Nicosia

  2. University of Florida, Gainesville, FL, USA

    Panos Pardalos

  3. Harvard University, Cambridge, MA, USA

    Renato Umeton

  4. Università di Catania, Catania, Catania, Italy

    Giovanni Giuffrida

  5. Almawave, Rome, Roma, Italy

    Vincenzo Sciacca

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Mikheev, Y. (2019). The Measure of Regular Relations Recognition Applied to the Supervised Classification Task. In: Nicosia, G., Pardalos, P., Umeton, R., Giuffrida, G., Sciacca, V. (eds) Machine Learning, Optimization, and Data Science. LOD 2019. Lecture Notes in Computer Science(), vol 11943. Springer, Cham. https://doi.org/10.1007/978-3-030-37599-7_16

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  • DOI: https://doi.org/10.1007/978-3-030-37599-7_16

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-37598-0

  • Online ISBN: 978-3-030-37599-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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