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Knowledge Discovery with Words Using Cartesian Granule Features: An Analysis for Classification Problems

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Data Mining, Rough Sets and Granular Computing

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 95))

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Abstract

Cartesian granule features were originally introduced to address some of the shortcomings of existing forms of knowledge representation such as decomposition error and transparency, and also to enable the paradigm modelling with words through related learning algorithms. This chapter presents a detailed analysis of the impact of granularity on Cartesian granule features models that are learned from example data in the context of classification problems. This analysis provides insights on how to effectively model problems using Cartesian granule features using various levels of granulation, granule characterizations, granule dimensionalies and granule generation techniques. Other modelling with words approaches such as the data browser [1, 2] and fuzzy probabilistic decision trees [3] are also examined and compared. In addition, this chapter provides a useful platform for understanding many other learning algorithms that may or may not explicitly manipulate fuzzy events. For example, it is shown how a naive Bayes classifier is equivalent to crisp Cartesian granule feature classifiers under certain conditions.

Part of the work reported here was carried out while at the Advance Computing Research Centre, University of Bristol, Bristol, UK. This part of the work was supported by the European Community Marie Curie Fellowship Program and by DERA (UK) under grant 92W69.

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Author information

Authors and Affiliations

  1. Grenoble Laboratory, Xerox Research Centre Europe (XRCE), 6 chemin de Maupteruis, 38240, Meylan, France

    James G. Shanahan

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  1. James G. Shanahan
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Editor information

Editors and Affiliations

  1. Department of Mathematics and Computer Science, San Jose State University The Metropolitan University of Silicon Valley, One Washington Square, 95192-0103, San Jose, CA, USA

    Tsau Young Lin

  2. Department of Computer Science, University of Regina, S4S 0A2, Regina, Saskatchewan, Canada

    Yiyu Y. Yao

  3. Computer Science Division and Electronics Research Laboratory Department of Electrical and Electronics, University of California Berkeley Initiative in Soft Computing (BISC), 94720-1776, Berkeley, CA, USA

    Lotfi A. Zadeh

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© 2002 Springer-Verlag Berlin Heidelberg

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Shanahan, J.G. (2002). Knowledge Discovery with Words Using Cartesian Granule Features: An Analysis for Classification Problems. In: Lin, T.Y., Yao, Y.Y., Zadeh, L.A. (eds) Data Mining, Rough Sets and Granular Computing. Studies in Fuzziness and Soft Computing, vol 95. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1791-1_3

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  • DOI: https://doi.org/10.1007/978-3-7908-1791-1_3

  • Publisher Name: Physica, Heidelberg

  • Print ISBN: 978-3-7908-2508-4

  • Online ISBN: 978-3-7908-1791-1

  • eBook Packages: Springer Book Archive

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