An empirical study on the incompetence of attribute selection criteria

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 1079, Issue , 1996, Pages 458-467

  Imam, Ibrahim F ^a  

^a GEORGE MASON UNIVERSITY   (United States)

Author keywords

Decision trees; Machine learning; Relevancy

Indexed keywords

DATABASE SYSTEMS; FORESTRY; INTELLIGENT SYSTEMS; LEARNING SYSTEMS; MACHINE LEARNING;

ATTRIBUTE SELECTION; DECISION CLASS; EMPIRICAL STUDIES; LEARNING PROCESS; MAIN TASKS; RELEVANCY;

DECISION TREES;

EID: 84949895302     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-61286-6_170     Document Type: Conference Paper

References (16)

1. Arciszewski, T, Bloedorn, E., Michalski, R., Mustafa, M., and Wnek, J., Constructive Induction in Structural Design, Report of the Machine Learning and Inference Labratory, MLI-92-7, Center for AI, George Mason Univerity, 1992.
2. Breiman, L., Friedman, J.H., Olshen, R.A., and Stone, C.J., Classification and Regression Trees, Belmont, California: Wadsworth Int. Group, 1984.
3. Cestnik, B., and Karalic, A., The Estimation of Probabilities in Attribute Selection Measures for Decision Tree Induction Proceedings of the European Summer School on Machine Learning, July 22-31, Priory Corsendonk, Belgium, 1991.
4. Fayyad, U.M., and Irani, K.B., On the Handling of Continous-Valued Attributes in Decision Tree Generation, Journal of Machine Learning, Vol. 8, No, 1, pp. 87-102, 1992.
5. Hart, A., Experience in the use of an inductive system in knowledge engineering, Research and Developments in Expert Systems, M, Bramer (Ed.), Cambridge, Cambridge University Press, 1984.
6. Imam, I.F. and Michalski, R.S., Learning Decision Trees from Decision Rules: A method and initial results from a comparative Study, in Journal of Intelligent Information Systems JIIS, Vol. 2, No. 3, pp. 279-304, Kerschberg, L., Ras, Z., & Zemankova, M. (Eds.), Kluwer Academic Pub., MA, 1993.
7. Imam, I.F., and Vafaie, H., An Empirical Comparison Between Global and Greey-Like Search for Feature Selection, proceeding of the 7th Florida AI Research Symposium, Florida, 1994.
8. Michaiski, R.S., Designing Extended Entry Decision Tables and Optimal Decision Trees Using Decision Diagrams, Technical Report No.898, Urbana: University of Illinois, March, 1978.
9. Mingers, J., An Empirical Comparison of selection Measures for Decision- Tree Induction, Machine Learning, Vol. 3, No. 3, pp. 319-342, Kluwer Academic Publishers, 1989.
10. Piatetsky-Shapiro, G., and Matheus, C.J., Measuring Data Dependencies in Large Databases, Proceedings of the AAAI-93 Workshop on Knowledge Discovery in Databases, pp. 162-174, Washington D.C,, 1993.
11. Quinlan, J.R., Discovering Rules By Induction from Large Collections of Examples, in D. Michie (Editor), Expert Systems in the Microelectronic Age, Edinburgh University Press, 1979.
12. Quinlan, J.R., Induction of Decision Trees, Machine Learning Vol. 1, No. 1, pp. 81-106, Kluwer Academic Publishers, 1986.
13. Quinlan, J.R., C4.5: Programs for Machine Learning, Morgan Kaufmann, Los Altos, California, 1993.
14. Sokai, R., and Rohlf, F., Biometry, Freeman Pub., San Francisco, 1981.
15. Thrun, S.B., Mitchell, T., and Cheng, J., (Eds.), The MONK's Problems: A Performance Comparison of Different Learning Algorithms, Technical Report, Carnegie Mellon University, October, 1991.
16. Ziarko, W., The Discovery, Analysis, and Representation of Data Dependencies in Databases, Knowledge Discovery In Databases, Shapiro, G., Frawley, W., (Eds.), AAAI Press, 1991.