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Title: Complexity modelling for case knowledge maintenance in case-based reasoning.
Authors: Massie, Stewart
Supervisors: Craw, Susan
Wiratunga, Nirmalie
Keywords: Case-based reasoning
Issue Date: Dec-2006
Publisher: The Robert Gordon University
Citation: MASSIE, S., CRAW, S. and WIRATUNGA, N., 2006. Complexity profiling for informed case-base editing. In: Proceedings of the 8th European Conference on Case-Based Reasoning. Springer. pp. 325-329.
WIRATUNGA, N., LOTHIAN, R. and MASSIE, S., 2006. Unsupervised feature selection for text data. In: Proceedings of the 8th European Conference on Case-Based Reasoning. Springer. pp. 340-354.
WIRATUNGA, N., MASSIE, S., CRAW, S., DONATI, A. and VICARI, E., 2006. Case based reasoning for anomaly report processing. In: Proceedings of 3rd Textual Case-Based Reasoning Workshop at the 8th European Conference on Case-Based Reasoning. Springer. pp. 44-49.
MASSIE, S., CRAW, S. and WIRATUNGA, N., 2005. Complexity guided case discovery for case based reasoning. In: Proceedings of the 20th National Conference on Artificial Intelligence. AAAI Press. pp. 216-221.
MASSIE, S., CRAW, S. and WIRATUNGA, N., 2004. A visualisation tool to explain case-base reasoning solutions for tablet formulation. In: Proceedings of the Twenty Fourth SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence. Springer. pp. 222-236.
WIRATUNGA, N., KOYCHEV, I. and MASSIE, S., 2004. Feature selection and generalisation for retrieval of textual cases. In: Proceedings of the 7th European Conference on Case-based Reasoning. Springer. pp. 806-820.
MASSIE, S., CRAW, S. and WIRATUNGA, N., 2004. Visualisation of case-base reasoning for explanation. In: Workshop Proceedings of the 7th European Conference on Case-based Reasoning. Springer. pp. 135-144.
MASSIE, S., CRAW, S. and WIRATUNGA, N., 2003. What is CBR competence? In: Poster presentation at the Twenty-third Annual Conference of the British Computer Society's Specialist Group on Artificial Intelligence. BCS Expert Update 8(1) pp. 7-10.
WIRATUNGA, N., CRAW, S. and MASSIE, S., 2003. Index driven selective sampling for case-based reasoning. In: Proceedings of the Fifth International Conference on Case-Based Reasoning. Springer. pp. 637-651.
Abstract: Case-based reasoning solves new problems by re-using the solutions of previously solved similar problems and is popular because many of the knowledge engineering demands of conventional knowledge-based systems are removed. The content of the case knowledge container is critical to the performance of case-based classification systems. However, the knowledge engineer is given little support in the selection of suitable techniques to maintain and monitor the case base. This research investigates the coverage, competence and problem-solving capacity of case knowledge with the aim of developing techniques to model and maintain the case base. We present a novel technique that creates a model of the case base by measuring the uncertainty in local areas of the problem space based on the local mix of solutions present. The model provides an insight into the structure of a case base by means of a complexity profile that can assist maintenance decision-making and provide a benchmark to assess future changes to the case base. The distribution of cases in the case base is critical to the performance of a case-based reasoning system. We argue that classification boundaries represent important regions of the problem space and develop two complexity-guided algorithms which use boundary identification techniques to actively discover cases close to boundaries. We introduce a complexity-guided redundancy reduction algorithm which uses a case complexity threshold to retain cases close to boundaries and delete cases that form single class clusters. The algorithm offers control over the balance between maintaining competence and reducing case base size. The performance of a case-based reasoning system relies on the integrity of its case base but in real life applications the available data invariably contains erroneous, noisy cases. Automated removal of these noisy cases can improve system accuracy. In addition, error rates can often be reduced by removing cases to give smoother decision boundaries between classes. We show that the optimal level of boundary smoothing is domain dependent and, therefore, our approach to error reduction reacts to the characteristics of the domain by setting an appropriate level of smoothing. We introduce a novel algorithm which identifies and removes both noisy and boundary cases with the aid of a local distance ratio. A prototype interface has been developed that shows how the modelling and maintenance approaches can be used in practice in an interactive manner. The interface allows the knowledge engineer to make informed maintenance choices without the need for extensive evaluation effort while, at the same time, retaining control over the process. One of the strengths of our approach is in applying a consistent, integrated method to case base maintenance to provide a transparent process that gives a degree of explanation.
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