Automating the construction of CBR systems using kernel methods

Instance‐based reasoning systems and, in general, case‐based reasoning systems are normally used in problems for which it is difficult to define rules. Although case‐based reasoning methods have proved their ability to solve different types of problems, there is still a demand for methods that facilitate their automation during their creation and the retrieval and reuse stages of their reasoning circle. This paper presents one method based on kernels, which can be used to automate some of the reasoning steps of instance‐based reasoning systems. Kernels were originally derived in the context of support vector machines, which identify the smallest number of data points necessary to solve a particular problem (e.g., regression or classification). Unsupervised kernel methods have been used successfully to identify the optimal instances to instantiate an instance base. The efficiency of the kernel model is shown on an oceanographic problem. © 2001 John Wiley & Sons, Inc.

[1]  Sankar K. Pal,et al.  Soft Computing in Case Based Reasoning , 2000, Springer London.

[2]  Corchado RodriÌguez,et al.  Neuro-symbolic model for real-time forecasting problems. , 2000 .

[3]  D. Y. Joh,et al.  CBR in a Changing Environment , 1997, ICCBR.

[4]  Edward E. Smith,et al.  Categories and concepts , 1984 .

[5]  Juan M. Corchado,et al.  Integrated Case-Based Neural Network Approach to Problem Solving , 1999, XPS.

[6]  Christopher K. Riesbeck,et al.  Inside Case-Based Reasoning , 1989 .

[7]  Juan M. Corchado,et al.  Adaptation of Cases for Case Based Forecasting with Neural Network Support , 2000, Soft Computing in Case Based Reasoning.

[8]  Bernhard Schölkopf,et al.  Sparse Kernel Feature Analysis , 2002 .

[9]  Enric Plaza,et al.  CasedBased Reasoningc an overview , 1997 .

[10]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[11]  E. R. Bareiss,et al.  PROTOS: An Experiment in Knowledge Acquisition for Heuristic ClassificationTasks , 1986 .

[12]  Ian D. Watson,et al.  Applying case-based reasoning - techniques for the enterprise systems , 1997 .

[13]  David Leake,et al.  Case-based reasoning research and development : Second International Conference on Case-Based Reasoning, ICCBR-97, Providence, RI, USA, July 25-27, 1997 : proceedings , 1997 .

[14]  Ramón López de Mántaras,et al.  Case-Based Reasoning: An Overview , 1997, AI Commun..

[15]  Janet L. Kolodner,et al.  Case-Based Reasoning , 1988, IJCAI 1989.

[16]  Agnar Aamodt,et al.  Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches , 1994, AI Commun..

[17]  Gunnar Rätsch,et al.  Input space versus feature space in kernel-based methods , 1999, IEEE Trans. Neural Networks.