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International Workshop on Rough Sets, Fuzzy Sets, Data Mining, and Granular-Soft Computing

RSFDGrC 2005: Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing pp 119–127Cite as

A Hierarchical Approach to Multimodal Classification

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Part of the Lecture Notes in Computer Science book series (LNAI,volume 3642)

Abstract

Data models that are induced in classifier construction often consists of multiple parts, each of which explains part of the data. Classification methods for such models are called the multimodal classification methods. The model parts may overlap or have insufficient coverage. How to deal best with the problems of overlapping and insufficient coverage? In this paper we propose hierarchical or layered approach to this problem. Rather than seeking a single model, we consider a series of models under gradually relaxing conditions, which form a hierarchical structure. To demonstrate the effectiveness of this approach we implemented it in two classifiers that construct multi-part models: one based on the so-called lattice machine and the other one based on rough set rule induction. This leads to hierarchical versions of the classifiers. The classification performance of these two hierarchical classifiers is compared with C4.5, Support Vector Machine (SVM), rule based classifiers (with the optimisation of rule shortening) implemented in Rough Set Exploration System (RSES), and a method combining k-nn with rough set rule induction (RIONA in RSES). The results of the experiments show that this hierarchical approach leads to improved multimodal classifiers.

Keywords

  • Hierarchical classification
  • multimodal classifier
  • lattice machine
  • rough sets
  • rule induction
  • k-NN

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  • DOI: 10.1007/11548706_13
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Author information

Authors and Affiliations

  1. Institute of Mathematics, Warsaw University, Banacha 2, 02-097, Warsaw, Poland

    Andrzej Skowron

  2. School of Computing and Mathematics, University of Ulster at Jordanstown, BT37 0QB, Northern Ireland, United Kingdom

    Hui Wang

  3. Institute of Informatics, Warsaw University, Banacha 2, 02-097, Warsaw, Poland

    Arkadiusz Wojna

  4. Institute of Mathematics, University of Rzeszów, Rejtana 16A, 35-310, Rzeszów, Poland

    Jan Bazan

Authors
  1. Andrzej Skowron
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  2. Hui Wang
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  3. Arkadiusz Wojna
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  4. Jan Bazan
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Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Regina, Regina, SK, S4S 0A2 Canada, Polish-Japanese Institute of Information Technology, Koszykowa 86, 02-008 Warsaw, P.O. Box, Poland

    Dominik Ślęzak

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

    JingTao Yao & Wojciech Ziarko & 

  3. Department of Electrical and Computer Engineering, University of Manitoba, R3T 5V6, Winnipeg, Manitoba, Canada

    James F. Peters

  4. College of Computer and Information Engineering, Hehan University, Henan, China

    Xiaohua Hu

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Skowron, A., Wang, H., Wojna, A., Bazan, J. (2005). A Hierarchical Approach to Multimodal Classification. In: Ślęzak, D., Yao, J., Peters, J.F., Ziarko, W., Hu, X. (eds) Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing. RSFDGrC 2005. Lecture Notes in Computer Science(), vol 3642. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11548706_13

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  • DOI: https://doi.org/10.1007/11548706_13

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28660-8

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