Self-optimising CBR retrieval

One reason why Case-Based Reasoning (CBR) has become popular is because it reduces development cost compared to rule-based expert systems. Still, the knowledge engineering effort may be demanding. In this paper we present a tool which helps to reduce the knowledge acquisition effort for building a typical CBR retrieval stage consisting of a decision-tree index and similarity measure. We use genetic algorithms to determine the relevance/importance of case features and to find optimal retrieval parameters. The optimisation is done using the data contained in the case-base. Because no (or little) other knowledge is needed this results in a self-optimising CBR retrieval. To illustrate this we present how the tool has been applied to optimise retrieval for a tablet formulation problem.

[1]  Ron Kohavi,et al.  The Utility of Feature Weighting in Nearest-Neighbor Algorithms , 1997 .

[2]  Susan Craw,et al.  Knowledge refinement to debug and maintain a tablet formulation system , 1997, Proceedings Ninth IEEE International Conference on Tools with Artificial Intelligence.

[3]  Giles Oatley.  An investigation of case-based reasoning for decision support of diagnosis in a large-scale ill-structured domain , 2000 .

[4]  A. Griffiths.  Introduction to Genetic Analysis , 1976 .

[5]  Jihoon Yang,et al.  Feature Subset Selection Using a Genetic Algorithm , 1998, IEEE Intell. Syst..

[6]  John Tait,et al.  A Case-Based Reasoning Tool For Vibration Analysis , 1999 .

[7]  Lawrence Davis,et al.  A Hybrid Genetic Algorithm for Classification , 1991, IJCAI.

[8]  Stefan Wess,et al.  Case-Based Reasoning Technology: From Foundations to Applications , 1998, Lecture Notes in Computer Science.

[9]  David W. Aha,et al.  Feature Weighting for Lazy Learning Algorithms , 1998 .

[10]  Padraig Cunningham,et al.  Knowledge engineering issues in developing a case-based reasoning application , 1999, Knowl. Based Syst..

[11]  Hiroshi Motoda,et al.  Feature Extraction, Construction and Selection: A Data Mining Perspective , 1998 .

[12]  Ron Kohavi,et al.  Irrelevant Features and the Subset Selection Problem , 1994, ICML.

[13]  Dorothea Heiss-Czedik,et al.  An Introduction to Genetic Algorithms. , 1997, Artificial Life.

[14]  Tony R. Martinez,et al.  Instance-Based Learning with Genetically Derived Attribute Weights , 1996 .

[15]  Tony R. Martinez,et al.  Improved Heterogeneous Distance Functions , 1996, J. Artif. Intell. Res..

[16]  David W. Aha,et al.  Weighting Features , 1995, ICCBR.

[17]  Thomas G. Dietterich,et al.  Efficient Algorithms for Identifying Relevant Features , 1992 .

[18]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[19]  David W. Aha,et al.  Feature Selection for Case-Based Classification of Cloud Types: An Empirical Comparison , 1994 .

[20]  Susan Craw,et al.  Genetic Algorithms to Optimise CBR Retrieval , 2000, EWCBR.

[21]  David Leake,et al.  Case-Based Reasoning: Experiences, Lessons and Future Directions , 1996 .

[22]  Pat Langley,et al.  Selection of Relevant Features and Examples in Machine Learning , 1997, Artif. Intell..

[23]  David B. Skalak,et al.  Prototype and Feature Selection by Sampling and Random Mutation Hill Climbing Algorithms , 1994, ICML.

[24]  Petri Koistinen,et al.  Kernel regression and backpropagation training with noise , 1991, [Proceedings] 1991 IEEE International Joint Conference on Neural Networks.