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Incremental Learning with Partial Instance Memory

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Foundations of Intelligent Systems (ISMIS 2002)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2366))

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Abstract

Agents that learn on-line with partial instance memory reserve some of the previously encountered examples for use in future training episodes. We extend our previous work by combining our method for selecting extreme examples with two incremental learning algorithms, aq11 and gem. Using these new systems, aq11-pm and gem-pm, and the task computer intrusion detection, we conducted a lesion study to analyze trade-offs in performance. Results showed that, although our partial-memory model decreased predictive accuracy by 2%, it also decreased memory requirements by 75%, learning time by 75%, and in some cases, concept complexity by 10%, an outcome consistent with earlier results using our partial-memory method and batch learning.

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Author information

Authors and Affiliations

  1. Department of Computer Science, Georgetown University, Washington, DC, 20057, USA

    Marcus A. Maloof

  2. Machine Learning and Inference Laboratory, School of Computational Sciences, George Mason University, Fairfax, VA, 22030, USA

    Ryszard S. Michalski

  3. Institute of Computer Science, Polish Academy of Sciences, Warsaw

    Ryszard S. Michalski

Authors
  1. Marcus A. Maloof
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  2. Ryszard S. Michalski
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Editor information

Editors and Affiliations

  1. UFR d’Informatique, Université Claude Bernard Lyon I, 8, boulevard Niels Bohr, 69622, Villeurbanne Cedex, France

    Mohand-Saïd Hacid

  2. Dept. of Computer Science College of IT, University of North Carolina, Charlotte, NC, 28223, USA

    Zbigniew W. Raś

  3. Băt. L. Equipe de Recherche en Ingénierie des Connaissances, Université Lumière Lyon 2, 5, avenue Pierre Mendes-France, 69676, Bron Cedex, France

    Djamel A. Zighed

  4. LRI, Université Paris Sud, Băt. 490, 91405, Orsay Cedex, France

    Yves Kodratoff

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© 2002 Springer-Verlag Berlin Heidelberg

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Maloof, M.A., Michalski, R.S. (2002). Incremental Learning with Partial Instance Memory. In: Hacid, MS., Raś, Z.W., Zighed, D.A., Kodratoff, Y. (eds) Foundations of Intelligent Systems. ISMIS 2002. Lecture Notes in Computer Science(), vol 2366. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48050-1_4

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  • DOI: https://doi.org/10.1007/3-540-48050-1_4

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-43785-7

  • Online ISBN: 978-3-540-48050-1

  • eBook Packages: Springer Book Archive

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