Abstract
The automatic inductive learning of production rules in a classification environment is a difficult process which requires several considerations and techniques to be studied. This is more noticeable when the learning process is applied to real world domains. Our goal is to focus and study some of the most important problems related to the automatic learning of production rules as well as to provide some tools for dealing with these problems. We first consider the data representation problem. Four different types of data are proposed. We then deal with the unsupervised case in which the data are observations of objects in the world and we pose three alternative mechanisms for clustering. If the data set contains examples and counter examples of some world concepts, the learning is called supervised. Within supervised learning we find the data redundancy problem. Two sorts of redundancy are studied: the one which is concerned with the set of examples, and the one which is concerned with the set of example descriptors.
Before we generate rules that describe the domain which is represented by the input data, we analyze the set of conditions which will be the basis of our rules. These conditions are called selectors and they enable us to control more directly the semantics of the induced rules. We have implemented several algorithms that generate selectors automatically and we have tested them together with four new rule generation algorithms. The results obtained are compared with those other results produced by other classical rule learning methods such as cn2 and c4.5rules.
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References
Langley, P.: Selection of relevant features in machine learning. In: Proceedings of the AAAI Fall Symposium on Relevance (1994)
Blum, A.L., Langley, P.: Selection of relevant features and examples in machine learning. Artificial Intelligence 97, 245–271 (1997)
Clark, P., Niblett, T.: The CN2 induction algorithm. Machine Learning Journal 3, 261–283 (1989)
Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Francisco (1993)
Riaño, D.: Automatic construction of descriptive rules. PhD thesis, Universitat Politècnica de Catalunya, Barcelona (1997), http://www.etse.urv.es/~drianyo/PhD.ps.Z
Valls, A., Riaño, D., Torra, V.: Sedàs: A semantic-based general classifier system. Mathware & Soft Computing 4, 267–279 (1997)
Kerlingen, F.N.: Foundations of Behavioural Research, 2nd edn., vol. 25, pp. 426–441. William Clowes & Sons Limited, Great Britain (1973)
Murphy, P.M., Aha, D.W.: UCI repository of machine learning databases, ftp address: ftp.ics.uci.edu, directory: /pub/machine learning databases/, e-mail address: ml-repository@ics.uci.edu
Gordon, A.D.: A review of hierarchical classification. Journal of the Royal Statistical Society A 150(2), 119–137 (1987)
Michalski, R.S., Stepp, R.E.: Learning from observation: conceptual clustering. In: Michalski, R.S., Carbonell, J.G., Mitchell, J.M. (eds.) Machine Learning: an Artificial Intelligence Approach, vol. 1, pp. 331–360. Morgan Kaufmann, Los Altos (1983)
Fisher, D.: Knowledge acquisition via incremental conceptual clustering. Machine Learning Journal 2, 139–172 (1987)
Cheeseman, P., Kelly, J., Self, M., Stutz, J., Taylor, W., Freeman, D.: Autoclass: a bayesian classification system. In: The Fifth international Conference on Machine Learning, pp. 54–64. Morgan Kaufmann Publishers, San Francisco (1988)
Sánchez, M., Cortés, U., Béjar, J., Grácia, J., Lafuente, J., Poch, M.: Concept formation in WWTP by means of classification techniques: A compared study. Applied Intelligence 7, 147–165 (1997)
Riaño, D., Cortés, U.: Rule generation and compactation in theWWTP problem. Computación y Sistemas 1(2), 77–89 (1997)
Muggleton, S.H.: Duce, an oracle-based approach to constructive induction. In: Proceedings of the Tenth International Joint Conference on Artificial Intelligence. Morgan Kaufmann, San Francisco (1987)
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© 1999 Springer-Verlag Berlin Heidelberg
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Riaño, D. (1999). On the Process of Making Descriptive Rules. In: Padget, J.A. (eds) Collaboration between Human and Artificial Societies. Lecture Notes in Computer Science(), vol 1624. Springer, Berlin, Heidelberg. https://doi.org/10.1007/10703260_11
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DOI: https://doi.org/10.1007/10703260_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-66930-2
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