Decision Tree Induction using Domain Knowledge Marlon Núñez Telefónica I+D, C/Emilio Vargas, 6, Madrid 28043, Spain, e-mail:
[email protected] (e-mail in 2017:
[email protected]) Abstract: At present, decision trees generated by algorithms of the ID3 family are accurate, however they can be incomprehensible to experts. There have been proposed various solutions to this problem. Cendrowska's PRISM and Gaines's INDUCT concentrated of finding the relevant values of attributes (by-passing trees), Quinlan (1989) concentrates on the relevant attributes overall, etc. The purpose of this paper is to show that the cause of this incomprehensibility is not only within the historical data, but also outside this data: in the domain knowledge. The EG2 algorithm (Núñez 1988a, 1988b) presented in this paper tries to generate more logical and convenient decision trees applying various types of generalization and at the same time optimizing the classification cost by means of the use of domain knowledge. The domain knowledge contains the ISA hierarchy and the measurement cost associated to each attribute. The user can define the degree of economy and generalization. This knowledge will influence directly on the quantity of search that the algorithm must undertake. This algorithm has been implemented and its results appear in this paper comparing them with other methods. How to cite this paper: Núñez, M. (1990), Decision tree induction using domain knowledge, in Wielinga B et al (eds) Current Trends in Knowledge Acquisition, IOS Press, 276-288 References Cendrowska, J. (1989). PRISM: An algorithm for inducing modular rules. Knowledge Acquisition for Knowledge Based Systems (Vol . 1). Gaines B. & Boose J. eds. Academic Press. Breiman, L. Friedman, J., Olshen, R & Stone, CJ. 1984. Classification and Regression trees. Belmont: Wadsworth. Duda R. et al. (1981). Model Design in the Prospector Exploration, Expert Systems in the Microelectronic Age, D. Edimburgh Univ. Press. Consultant System for Michie (ed). Edimburgh Mineral Scottland. Gaines. 3., 1989. Knowledge Acquisition: Expertise Transfer. Proceedings of the EKAW, The Continuum Linking Machine Learning and 89. Boose 1., Gaines, B.. Ganascia. J. (eds). Forsyth R. and Rada R. (1986). Machine Leaming Applications in Expert Systems Information Retrieval (pp 60-64), Ellis Horwood Publishers. West Sussex, England. Hunt E.B., Marin J. Stone PJ. (1966). Experiments in induction, New York, Academic Press. Kodratoff Y. and Ganascia J. (1986). Improving the Generalization Step in Learning, Machine Learning Vol 2, Michalski R.S. Carbonell in Mitchell T.M. eds., Morgan Kauffman, Los Altos Kononenko. 1.. Bratko, 1., & Roskar, E. 1984. Experiments in automatic learning of medical diagnostic rules. Technical Report. Jozef Stefan Institute. Ljubljana, Yugoslavia. Lavrac, N, Mozetic. I & Kononenko, 1986. An Experimental comparison of two learning Proceedings of ISSEK Workshop 86, Turing Institute. Larson and Michalski R. (1977). Inductive Inference of VL Decision Rules. Proceedings of the WPDIS-77, Sigart, Newsletter 63. Michalski R. (1983). Theory and Methodology of Inductive Learning" Machine Learning Vol 1. Michalski R.S. Carbonell J.G. Mitchell T.M. eds. Morgan Kauffman. Los Altos (CA). Nuñez, M. (1988). Economic Induction: A Case Study, in Proceedings of the Third European Working Session on Learning, Derek Sleeman (ed), Pitman Publishing, London. Nuñez, M. (1988b). El Método de Aprendizaje EG2: Una Aplicacion de Conociminento de Base a Ejemplos Estructurados, Master Thesis in Knowledge Engineering, Universidad Politecnica de Madrid. Quinlan. J .R. (1979). Discovering Rules by Induction from Large collection of Examples, m Expert Systems in the microelectronic Age. D. Michie (ed), Edimburg University Press. Quinlan. J.R. (1986). Induction of decision trees. Machine Learning, 1.1. Quinlan. J.R. (1989). Simplifying decision trees. Knowledge Acquisition for Knowledge Based Systems (Vol . 1) . Gaines B. & Boose J. eds. Academic Press. Shannon C.E. & Warren Weaver. (1971). The Mathematical Theory of Communication, University of Illinois Press. Tan M. and Schlimmer J. (1989). Cost-Sensitive Concept Learning of Sensor Use in Approach and Recognition. in the Proceedings of the Sixth International, Workshop on Machine Learning, Ithaca, New York. Winston, F. H. (1977). Artificial Intelligence, Addison-Wesley.
