Abstract
A hybrid classification system is a system composed of several intelligent techniques such that the inherent limitations of one individual technique be compensated for by the strengths of another technique. In this paper, we investigate the outline of a hybrid diagnostic system for Attention Deficit Disorder (ADD) in children. This system uses Rough Sets (RS) and Modified Rough Sets (MRS) to induce rules from examples and then uses our modified genetic algorithms to globalize the rules. Also, the classification capability of this hybrid system was compared with the behavior of (a) another hybrid classification system using RS, MRS, and the “dropping condition” approach, (b) the Interactive Dichotomizer 3 (ID3) approach, and (c) a basic genetic algorithm.
The results revealed that the global rules generated by the hybrid system are more effective in classification of the testing dataset than the rules generated by the above approaches.
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References
Catlin D. E., “Estimation, Control, and the Discrete Kaiman Filter,” Springer-Verlog, 1989.
Cover T. M. and Thomas J. A., “Elements of Information Theory,” John Wily and Sons, 1991.
Efron B., “Bootstrap Methods: Another look at the Jacknife,” Ann Statistics, 7: 1–26, 1979.
Efron B., Tibshirani R., “Bootstrap Methods for Standard Errors, Confidence Intervals, and Other measures of Statistical Accuracy,” Statistical Science, pp 54–77, 1985.
Fayyad M., Irani B., “On the Handling of Continuous-Valued Attributes in Decision Tree Generation,” Intl. J. of Machine Learning, 8: 87–102, 1992.
Goonatilake S. and Khebbal S. (ed), “Intelligent Hybrid Systems,” John Wily and Sons, 1995.
Grzymala-Busse J. W., “The Rule Induction System LERS Q: A Version for Personal Computers,” Proc. of The Intl. Workshop on Rough Sets and Knowledge Discovery, Baniff, Alberta, Canada, p 509, 1993.
Hashemi R. R., Le Blanc L., Rucks C., Sheary A., “A Neural Network for Transportation Safety Modeling,” Intl. J. of Expert Systems With Applications 9(3): 247–256, 1995.
Hashemi R. R., Pearce B. A., Hinson W. G., Paule M. G., and Young J. F., “IQ Estimation of Monkeys Based on Human Data Using Rough Sets,” Proc. of The Intl. Workshop on Rough Sets and Soft Computing, San Jose, California, pp 400–407, 1994.
Hashemi R. R., Jelovsek F. R., Razzaghi M., “Developmental Toxicity Risk Assessment: A Rough Sets Approach,” Intl. J. of Methods of Information in Medicine, 32: 47–54, 1993.
Hashemi R. R., Pearce B. A., Arani R. B., Hinson W. G., and Paule M. G., “A Rough-Genetic Approach for classification of Complex data” Proc. of The Symposium on Applied Computing, Philadelphia, PA, pp 124–130, 1996.
Hashemi R. R., Jelovsek F. R., “Inductive Learning From Examples: A Rough Sets Approach,” Proc. of The 1991 ACM/IEEE Intl. Symposium on Applied Computing, Kansas City, Missouri, pp 346–349, 1991.
Hashemi R. R., Jelovsek F. R., Razzaghi M., Talburt J. R., “Conflict Resolution in Rule Learning From Examples,” Proc. of The 1992 ACM Conference on Applied Computing, Kansas City, Missouri, pp 598–602, 1992.
Packard N. H., “A Genetic Learning Algorithm for the Analysis of Complex Data,” Complex Systems, 4:543–572, 1990.
Paule, M.G., “Analysis of Brain Function Using a Battery of Schedule- Controlled Operant Behaviors,” Neurobehavioral Toxicity: Analysis and Interpretation, B. Weiss and J. O’Donoghue, Eds., Raven Press, New York, pp. 331–338, 1994.
Paule, M.G., “Approaches to Utilizing Aspects of Cognitive Function as Indicators of Neurotoxicity,” Neurotoxicology: Approaches and Methods, L. Chang and W. Slikker Jr., Eds., Academic Press, Orlando, FL., pp. 301–308, 1995.
Paule, M.G., Cranmer, J.M., Wilkins, J.D., Stern, H.P, and Hoffman, E.L., “Quantitation of Complex Brain Function in Children: Preliminary Evaluation Using a Nonhuman Primate Behavioral Test Battery,” Neurotoxicology 9(3): 367–378, 1988.
Paule, M.G., Forrester, T.M., Mäher, M.A., Cranmer, J.M., and Allen, R.R., “Monkey Versus Human Performance in the NCTR Operant Test Battery,” Neurotoxicol. Teratol. 12(5): 503–507, 1990
Pawlak Z., “Rough Classification,” Intl. J. of Man-Machine Studies, 20:469–483, 1984.
Pawlak Z., Slowinski K., and Slowinski R., “Rough Classification of Patients After Highly Selective Vagotomy for Duodenal Ulcer,” Intl. J. of Man-Machine Studies, 24:413–433, 1986.
Quinlan R. J., “Discovering Rules by Induction From Large Collections of Examples,” Expert Systems in the Micro-Electronic Age, Edinburg University Press, Edinburg, pp 168–201, 1979.
Zurada J. M., “Introduction to Artificial Neural Systems,” West Publishing Co., 1992.
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Hashemi, R.R., Pearce, B.A., Arani, R.B., Hinson, W.G., Paule, M.G. (1997). A Fusion of Rough Sets, Modified Rough Sets, and Genetic Algorithms for Hybrid Diagnostic Systems. In: Rough Sets and Data Mining. Springer, Boston, MA. https://doi.org/10.1007/978-1-4613-1461-5_9
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DOI: https://doi.org/10.1007/978-1-4613-1461-5_9
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