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A comparison of two MLEM2 rule induction algorithms extended to probabilistic approximations

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Abstract

A probabilistic approximation is a generalization of the standard idea of lower and upper approximations, defined for equivalence relations. Recently probabilistic approximations were additionally generalized to an arbitrary binary relation so that probabilistic approximations may be applied for incomplete data. We discuss two ways to induce rules from incomplete data using probabilistic approximations, by applying true MLEM2 algorithm and an emulated MLEM2 algorithm. In this paper we report novel research on a comparison of both approaches: new results of experiments on incomplete data with three interpretations of missing attribute values. Our results show that both approaches do not differ much.

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Acknowledgments

This work was partially supported by the Polish National Science Centre grant DEC-2013/09/B/ST6/01568 and by the Centre for Innovation and Transfer of Natural Sciences and Engineering Knowledge of University of Rzeszow, Poland.

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Authors and Affiliations

  1. Department of Electrical Engineering and Computer Science, University of Kansas, Lawrence, KS, 66045, USA

    Patrick G. Clark & Jerzy W. Grzymala-Busse

  2. Department of Expert Systems and Artificial Intelligence, University of Information Technology and Management, 35–225, Rzeszow, Poland

    Jerzy W. Grzymala-Busse

  3. Interdisciplinary Centre for Computational Modeling, Rzeszow University, 35–310, Rzeszow, Poland

    Wojciech Rzasa

Authors
  1. Patrick G. Clark
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  2. Jerzy W. Grzymala-Busse
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  3. Wojciech Rzasa
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Correspondence to Jerzy W. Grzymala-Busse.

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Clark, P.G., Grzymala-Busse, J.W. & Rzasa, W. A comparison of two MLEM2 rule induction algorithms extended to probabilistic approximations. J Intell Inf Syst 47, 515–529 (2016). https://doi.org/10.1007/s10844-015-0385-0

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  • DOI: https://doi.org/10.1007/s10844-015-0385-0

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