Abstract
Mental fit of classification rules is lately introduced to judge the adequacyof such rules for human understanding. This paper first discusses the various criteria introduced in relation to mental fit in the literature. Based on this, the paper derives a general criterion for the interpretability of partitions generated by classification rules. We introduce interpretability as a combination of mental fit and data fit, or more specifically, as a combination of comprehensibility and reliability of a partition. Weintroduce so-called prototypes to improve comprehensibility, and the so-called reliability of such prototypes as a measure of data fit.
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References
AAMODT, A., and PLAZA, E (1994): Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches AI Communications, 7(1):39–59.
BODENHOFER, U., and BAUER, P. (1999): Towards an Axiomatic Treatment of “Interpretability”, in Proceedings of the 6th International Conference on Soft Computing (IIZUKA2000), Iizuka, Japan, 334–339.
BREIMAN, L., FRIEDMAN, J. H., OLSHEN, R. A., and STONE, C. J. (1984): Classification and Regression Trees, Wadsworth, Belmont.
CORNELL, J. A. (1990): Experiments with mixtures. 2nd edition, Wiley.
EIJKEL, VAN DEN G. (1999): Rule Induction, in Berthold, Hand (Eds.): Intelligent Data Analysis: An Introduction, Springer, Berlin, 195–216.
FENG, C. and MICHIE, D. (1994): Machine Learning of Rules and Trees, in Michie, Spiegelhalter, and Taylor (Eds.): Machine Learning, Neural and Statistical Classification, Ellis Horwood, New York, 50–83.
FUKUNAGA, K. (1990): Introduction to Statistical Pattern Recognition, Academic Press, New York.
HAND, D. J. (1997): Construction and Assessment of Classification Rules, Wiley, Chichester
LU, H., SETIONO, R., and LIU, H. (1995): Neuro Rule: A Connectionist Approach to Data Mining, in Proceedings of the 1st VLDB Conference Zürich, Switzerland, 1995
MICHIE, D., SPIEGELHALTER, D. J., and TAYLOR, C. C. (1994): Conclusions in Michie, Spiegelhalter, and Taylor (Eds.): Machine Learning, Neural and Statistical Classification, Ellis Horwood, New York, 213–227.
RISSANEN, J. (1978): Modeling by Shortest Data Description. Automatica 14, 465–471.
SILBERSCHATZ, A., and TUZHILIN, A. (1996): What makes patterns interesting in knowledge discovery systems. IEEE Transactions on Knowledge and Data Engineering, 8, 970–974.
SMYTH, P. and GOODMAN, R. (1992): An information theoretic approach to rule induction from databases. IEEE Transactions on Knowledge and Data Engineering, 4, 301–316
SONDHAUß, U. and WEIHS, C. (2001): Standardizing the Comparison of Partitions, submitted to Journal of Computational Statistics.
WANG, K., TAY, W., and LIU, B. (1998): An interestingness-based interval merger for numeric association rules, in Proceedings of the International Conference on Knowledge Discovery and Data Mining, August 1998, New York City, AAAI, 121–128.
WEIHS, C. (1992): Vorhersagefähigkeit multivariater linearer Methoden: Simulation und Grafik, in Enke, Gölles, Haux and Wernecke (Eds.): Methoden und Werkzeuge für die exploratorische Datenanalyse in den Biowissenschaften, Fischer, Stuttgart, 111–127.
WEIHS, C. (1993): Multivariate Exploratory Data Analysis and Graphics: A Tutorial. Journal of Chemometrics 7, 305–340.
WEIHS, C., ROEHL, M. C., and THEIS, W. (1999): Multivariate classification of business phases, Technical Report 26/1999, SFB 475, Universität Dortmund.
WEISS, S. M. and KULIKOWSKI, C. A. (1991): Computer Systems that Learn; Morgan Kaufmann, San Francisco, 114, 168/9.
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Weihs, C., Sondhauss, U.M. (2003). Combining Mental Fit and Data Fit for Classification Rule Selection. In: Schwaiger, M., Opitz, O. (eds) Exploratory Data Analysis in Empirical Research. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-55721-7_21
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DOI: https://doi.org/10.1007/978-3-642-55721-7_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-44183-0
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