A Knowledge-Light Approach to Regression Using Case-Based Reasoning

  • Neil McDonnell
  • Pádraig Cunningham
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4106)


Most CBR systems in operation today are ‘retrieval-only’ in that they do not adapt the solutions of retrieved cases. Adaptation is, in general, a difficult problem that often requires the acquisition and maintenance of a large body of explicit domain knowledge. For certain machine-learning tasks, however, adaptation can be performed successfully using only knowledge contained within the case base itself. One such task is regression (i.e. predicting the value of a numeric variable). This paper presents a knowledge-light regression algorithm in which the knowledge required to solve a query is generated from the differences between pairs of stored cases. Experiments show that this technique performs well relative to standard algorithms on a range of datasets.


Problem Domain Domain Space Nominal Attribute Local Linear Regression Difference Case 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Neil McDonnell
    • 1
  • Pádraig Cunningham
    • 1
  1. 1.Department of Computer ScienceTrinity College DublinIreland

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