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A Dynamic Approach to Reducing Dialog in On-Line Decision Guides

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Advances in Case-Based Reasoning (EWCBR 2000)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1898))

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Abstract

Online decision guides typically ask too many questions of the user, as they make no attempt to focus the questions. We describe some approaches to minimising the questions asked of a user in an online query situation. Questions are asked in an order that reflects their ability to narrow down the set of cases. Thus time to reach an answer is decreased. This has the dual benefit of taking some of the monotony out of online queries, and also of decreasing the amount of network request-response cycles. Most importantly, question order is decided at run time, and therefore adapts to the user. This approach is in the spirit of lazy learning with induction delayed to run-time, allowing adaptation to the emerging details of the situation. We evaluate a few different approaches to the question selection task, and compare the best approach (one based on ideas from retrieval in CBR) to a commercial online decision guide.

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References

  1. Aha D., Bankert R. (1995) A Comparative Evaluation of Sequential Feature Selection Algorithms, in proceedings of AI & Statistics Workshop 1995.

    Google Scholar 

  2. Bishop C.M. (1995) Neural Networks for Pattern Recognition, Oxford University Press, Oxford, 1995.

    Google Scholar 

  3. Cunningham P., Smyth B. (1994) A Comparison of model-based and incremental case-based approaches to electronic fault diagnosis, in proceedings of the Case-Based Reasoning Workshop, AAAI-1994.

    Google Scholar 

  4. Cunningham P., Carney, J. (2000) Diversity versus Quality in Classification Ensembles based on Feature Selection, to be presented at European Conference on Machine Learning, Barcelona Spain, June 2000.

    Google Scholar 

  5. Dash M., Liu H., Yao J. (1997) Dimensionality Reduction for Unsupervised Data, in proceedings of IEEE International Conference on Tools with AI (TAI-97), pp. 532–539.

    Google Scholar 

  6. Dash M., Liu H. (1999) Handling Large Unsupervised Data via Dimensionality Reduction, in proceedings of SIGMOD Data Mining and Knowledge Discovery Workshop, (DMDK), Philadelphia, USA, May 1999.

    Google Scholar 

  7. Devaney M., Ram, A. (1997) Efficient Feature Selection in Conceptual Clustering, in proceedings of the 14th International Conference on Machine Learning, Nashville, 1997.

    Google Scholar 

  8. Doyle M., Cunningham P. (2000) A Dynamic Approach to Reducing Dialog in On-Line Decision Guides, (extended version of this paper) Trinity College Dublin Computer Science Technical Report TCD-CS-2000-14, http://www.cs.tcd.ie/.

  9. Everitt B.S. (1993) Cluster Analysis, 3rd Ed., Edward Arnold.

    Google Scholar 

  10. Fast J.D. (1970) Entropy: the significance of the concept of entropy and its applications in science and technology, chapter 2: The Statistical Significance of the Entropy Concept. Philips Technical Library, Eindhoven.

    Google Scholar 

  11. Fisher D.H. (1987) Knowledge Acquisition via incremental conceptual clustering, in Machine Learning, Vol. 2., pp. 139–172.

    Google Scholar 

  12. Kaufman L., Rousseeuw P. (1990) Finding Groups in Data: An Introduction to Cluster Analysis, Wiley, New York.

    Google Scholar 

  13. Quinlan J.R. (1993) C4.5: Programs for Machine Learning, Morgan Kaufmann.

    Google Scholar 

  14. Smyth B., Cunningham P. (1994) A Comparison of Incremental Case-Based Reasoning and Inductive Learning, in proceedings of the 2nd European Workshop on Case-Based Reasoning, Chantilly, France, November 1994.

    Google Scholar 

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© 2000 Springer-Verlag Berlin Heidelberg

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Doyle, M., Cunningham, P. (2000). A Dynamic Approach to Reducing Dialog in On-Line Decision Guides. In: Blanzieri, E., Portinale, L. (eds) Advances in Case-Based Reasoning. EWCBR 2000. Lecture Notes in Computer Science, vol 1898. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44527-7_6

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  • DOI: https://doi.org/10.1007/3-540-44527-7_6

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-67933-2

  • Online ISBN: 978-3-540-44527-2

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