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Decision Concept Lattice vs. Decision Trees and Random Forests

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Formal Concept Analysis (ICFCA 2021)

Abstract

Decision trees and their ensembles are very popular models of supervised machine learning. In this paper we merge the ideas underlying decision trees, their ensembles and FCA by proposing a new supervised machine learning model which can be constructed in polynomial time and is applicable for both classification and regression problems. Specifically, we first propose a polynomial-time algorithm for constructing a part of the concept lattice that is based on a decision tree. Second, we describe a prediction scheme based on a concept lattice for solving both classification and regression tasks with prediction quality comparable to that of state-of-the-art models.

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Notes

  1. 1.

    https://scikit-learn.org/stable/modules/ensemble.html#random-forests.

  2. 2.

    http://h2o-release.s3.amazonaws.com/h2o/master/1752/docs-website/datascience/rf.html.

  3. 3.

    https://docs.rapids.ai/api/cuml/stable/api.html#random-forest.

References

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  3. Sci-kit learn description of random forest. https://scikit-learn.org/stable/modules/ensemble.html#parameters

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Acknowledgments

The work of Sergei O. Kuznetsov on the paper was carried out at St. Petersburg Department of Steklov Mathematical Institute of Russian Academy of Science and supported by the Russian Science Foundation grant no. 17-11-01276

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Correspondence to Egor Dudyrev or Sergei O. Kuznetsov .

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Dudyrev, E., Kuznetsov, S.O. (2021). Decision Concept Lattice vs. Decision Trees and Random Forests. In: Braud, A., Buzmakov, A., Hanika, T., Le Ber, F. (eds) Formal Concept Analysis. ICFCA 2021. Lecture Notes in Computer Science(), vol 12733. Springer, Cham. https://doi.org/10.1007/978-3-030-77867-5_16

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  • DOI: https://doi.org/10.1007/978-3-030-77867-5_16

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  • Print ISBN: 978-3-030-77866-8

  • Online ISBN: 978-3-030-77867-5

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