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Maintaining Case-Based Reasoning Systems: A Machine Learning Approach

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Advances in Case-Based Reasoning (ECCBR 2004)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3155))

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Abstract

Over the years, many successful applications of case-based reasoning (CBR) systems have been developed in different areas. The performance of CBR systems depends on several factors, including case representation, similarity measure, and adaptation. Achieving good performance requires careful design, implementation, and continuous optimization of these factors. In this paper, we propose a maintenance technique that integrates an ensemble of CBR classifiers with spectral clustering and logistic regression to improve the classification accuracy of CBR classifiers on (ultra) high-dimensional biological data sets.

Our proposed method is applicable to any CBR system; however, in this paper, we demonstrate the improvement achieved by applying the method to a computational framework of a CBR system called \(\mathit{TA3}\). We have evaluated the system on two publicly available microarray data sets that cover leukemia and lung cancer samples. Our maintenance method improves the classification accuracy of \(\mathit{TA3}\) by approximately 20% from 65% to 79% for the leukemia and from 60% to 70% for the lung cancer data set.

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of Toronto, 10 King’s College Road, Toronto, Ontario, M5S 3G4, Canada

    Niloofar Arshadi & Igor Jurisica

  2. Division of Cancer Informatics, Ontario Cancer Institute, Princess Margaret Hospital, University Health Network, 610 University Avenue, Toronto, Ontario, M5G 2M9, Canada

    Igor Jurisica

Authors
  1. Niloofar Arshadi
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  2. Igor Jurisica
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Electronics, Mälardalen University, SE-72123, Västerås, Sweden

    Peter Funk

  2. Facultad de Informática, Universidad Complutense de Madrid, 28040, Madrid, Spain

    Pedro A. González Calero

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Arshadi, N., Jurisica, I. (2004). Maintaining Case-Based Reasoning Systems: A Machine Learning Approach. In: Funk, P., González Calero, P.A. (eds) Advances in Case-Based Reasoning. ECCBR 2004. Lecture Notes in Computer Science(), vol 3155. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28631-8_3

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  • DOI: https://doi.org/10.1007/978-3-540-28631-8_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22882-0

  • Online ISBN: 978-3-540-28631-8

  • eBook Packages: Springer Book Archive

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