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On Combined Classifiers, Rule Induction and Rough Sets

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Transactions on Rough Sets VI

Part of the book series: Lecture Notes in Computer Science ((TRS,volume 4374))

Abstract

Problems of using elements of rough sets theory and rule induction to create efficient classifiers are discussed. In the last decade many researches attempted to increase a classification accuracy by combining several classifiers into integrated systems. The main aim of this paper is to summarize the author’s own experience with applying one of his rule induction algorithm, called MODLEM, in the framework of different combined classifiers, namely, the bagging, n 2–classifier and the combiner aggregation. We also discuss how rough approximations are applied in rule induction. The results of carried out experiments have shown that the MODLEM algorithm can be efficiently used within the framework of considered combined classifiers.

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Authors and Affiliations

  1. Institute of Computing Science, Poznań University of Technology, 60-965 Poznań, ul.Piotrowo 2, Poland

    Jerzy Stefanowski

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  1. Jerzy Stefanowski
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James F. Peters Andrzej Skowron Ivo Düntsch Jerzy Grzymała-Busse Ewa Orłowska Lech Polkowski

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Stefanowski, J. (2007). On Combined Classifiers, Rule Induction and Rough Sets. In: Peters, J.F., Skowron, A., Düntsch, I., Grzymała-Busse, J., Orłowska, E., Polkowski, L. (eds) Transactions on Rough Sets VI. Lecture Notes in Computer Science, vol 4374. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71200-8_18

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  • DOI: https://doi.org/10.1007/978-3-540-71200-8_18

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-71198-8

  • Online ISBN: 978-3-540-71200-8

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