Evaluation Measures for TCBR Systems

  • M. A. Raghunandan
  • Nirmalie Wiratunga
  • Sutanu Chakraborti
  • Stewart Massie
  • Deepak Khemani
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5239)


Textual-case based reasoning (TCBR) systems where the problem and solution are in free text form are hard to evaluate. In the absence of class information, domain experts are needed to evaluate solution quality, and provide relevance information. This approach is costly and time consuming. We propose three measures that can be used to compare alternate TCBR system configurations, in the absence of class information. The main idea is to quantify alignment as the degree to which similar problems have similar solutions. Two local measures capture this information by analysing similarity between problem and solution neighbourhoods at different levels of granularity, whilst a global measure achieves the same by analyzing similarity between problem and solution clusters. We determine the suitability of the proposed measures by studying their correlation with classifier accuracy on a health and safety incident reporting task. Strong correlation is observed with all three approaches with local measures being slightly superior over the global one.


Case Base Solution Space Class Label Alignment Measure Alignment Profile 
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 2008

Authors and Affiliations

  • M. A. Raghunandan
    • 1
  • Nirmalie Wiratunga
    • 2
  • Sutanu Chakraborti
    • 3
  • Stewart Massie
    • 2
  • Deepak Khemani
    • 1
  1. 1.Department of Computer Science and EngineeringIndian Institute of TechnologyMadrasIndia
  2. 2.School of ComputingThe Robert Gordon UniversityScotlandUK
  3. 3.Tata Research Development and Design CentrePuneIndia

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