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Reasoning with unknown, not-applicable and irrelevant meta-values in concept learning and pattern discovery

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Abstract

This paper describes methods for reasoning with unknown, irrelevant, and not-applicable meta-values when learning concept descriptions from examples or discovering patterns in data. These types of meta-values represent different reasons for which regular values are not available, thus require different treatment in both rule learning and rule testing. The presented methods are handled internally, within the learning and testing algorithms, and not in preprocessing as those widely described in the literature. They have been implemented in the AQ21 multitask learning and knowledge discovery program, and experimentally tested on three real world and one designed datasets.

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Notes

  1. We made the following distinction between concept learning and pattern discovery: in concept learning, one seeks a general concept descriptions that account for all concept examples in the training data; while in pattern discovery one seeks strong or “interesting” regularities in the data.

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Acknowledgements

The authors thank Dr. Kenneth Kaufman for his useful comments on the earlier version of this paper, and for valuable suggestions regarding examples used to illustrate the methodology. Jarek Pietrzykowski helped to prepare data for experiments involving the computer users and ROBOTS datasets. This paper is a significantly modified and improved version of the Technical Report MLI-05-1 of Machine Learning and Inference Laboratory, George Mason University (Michalski and Wojtusiak 2006).

The authors thank anonymous reviewers that helped improve the manuscript, in particular sections concerned with research related to meta-values.

Research presented here was partially conducted at the Machine Learning and Inference Laboratory of George Mason University and partially at the Hanse Institute for Advanced Study in Delmenhorst and at the University of Bremen in the Collaborative Research Center 637. Research activities of the Machine Learning and Inference Laboratory have been supported in part by the National Science Foundation Grants No. IIS 9906858 and IIS 0097476, and in part by the UMBC/LUCITE #32 grant. The findings and opinions expressed here are those of the authors, and do not necessarily reflect those of the above sponsoring organizations.

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Authors and Affiliations

  1. Machine Learning and Inference Laboratory, George Mason University, Fairfax, VA, USA

    Ryszard S. Michalski & Janusz Wojtusiak

  2. Institute of Computer Science, Polish Academy of Sciences, Warsaw, Poland

    Ryszard S. Michalski

  3. Department of Health Administration and Policy, George Mason University, Fairfax, VA, USA

    Janusz Wojtusiak

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  1. Ryszard S. Michalski
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  2. Janusz Wojtusiak
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Correspondence to Janusz Wojtusiak.

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This paper has been revised and submitted by the second author after death of Professor Ryszard S. Michalski in 2007.

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Michalski, R.S., Wojtusiak, J. Reasoning with unknown, not-applicable and irrelevant meta-values in concept learning and pattern discovery. J Intell Inf Syst 39, 141–166 (2012). https://doi.org/10.1007/s10844-011-0186-z

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