Abstract
Machine learning has been proven useful for solving the bottlenecks in building expert systems. Noise in the training instances will, however, confuse a learning mechanism. Two main steps are adopted here to solve this problem. The first step is to appropriately arrange the training order of the instances. It is well known from Psychology that different orders of presentation of the same set of training instances to a human may cause different learning results. This idea is used here for machine learning and an order arrangement scheme is proposed. The second step is to modify a conventional noise-free learning algorithm, thus making it suitable for noisy environment. The generalized version space learning algorithm is then adopted to process the training instances for deriving good concepts. Finally, experiments on the Iris Flower problem show that the new scheme can produce a good training order, allowing the generalized version space algorithm to have a satisfactory learning result.
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References
Atkinson, R.L., Atkinson, R.C., Smith, E.E. and Bem, D.J. (1990), Introduction to Psychology, Tenth Edition, Harcount Brace Jovanovich, Inc.
Baddeley, A. (1990), Human Memory Theory and Practice. MA: Allyn and Bacon.
Breiman, L., Friedman, J.H., Olshen, R.A. and Stone, C.J. (1984), Classification and Regression Trees. CA: Wadsworth.
Chang, F. (1992), 'From artificial intelligence to cognitive science,' Science Monthly 23(2), pp. 108–113.
Chang, K.C., Hong, T.P. and Tseng, S.S. (1996), 'Machine Learning by Imitating Human Learning,' Minds and Machines 6(2), pp. 203–228.
Clark, P. and Niblett, T. (1989), 'The CN2 induction algorithm,' Machine Learning 3, pp. 261–283.
Dasarathy, B.V. (1980), 'Noise around the neighborhood: a new system structure and classification rule for recognition in partially exposed environments,' IEEE Transactions on Pattern Analysis and Machine Intelligence 2(1), pp. 67–71.
Feigenbaum, E.A. (1977), 'The art of artificial intelligence: themes and case studies of knowledge engineering,' Proceedings of the Fifth International Joint Conference on Artificial Intelligence, Cambridge, MA, pp. 1014–1029.
Fisher, G.H. (1967), 'Preparation of ambiguous stimulus materials,' Perception and Psychophysics 2, pp. 421–422.
Fisher, R.A. (1936), 'The use of multiple measurements in taxonomic problems,' Annual Eugenics 7, pp. 179–188.
Hall, J.F. (1989), Learning and Memory. MA: Allyn and Bacon.
Hirsh, H. (1989), Incremental Version-Space Merging: A general Framework for Concept Learning. Ph.D. Thesis, Stanford University.
Hirsh, H. (1994), 'Generalizing version space,' Machine Learning 17, pp. 5–46.
Hong, T.P. (1992), A Study of Parallel Processing and Noise Management on Machine Learning. Ph.D. Thesis, National Chiao Tung University, Taiwan, R.O.C.
Hong, T.P. and Tseng, S.S. (1994) 'Learning concepts in parallel based upon the strategy of version space,' IEEE Transactions on Knowledge and Data Engineering 6(6), pp. 857–867.
Hong, T.P. and Tseng, S.S. (1997), 'A generalized version space learning algorithm for noisy and uncertain data,' IEEE Transactions on Knowledge and Data Engineering 9(2), pp. 336–340.
Hong, T.P. and Chen, J.B. 'Finding relevant attributes and membership functions,' accepted and to appear in Fuzzy Set and Systems.
Kibler, D. and Langley, P. (1988), 'Machine learning as an experimental science,' Proceedings of the European Working Session on Learning, pp. 87–92.
Kodratoff, Y., Manago, M.V. and Blythe, J. (1987), 'Generalization and noise,' International Journal of Man-Machine Studies 27, pp. 181–204.
Mingers, J. (1989), 'An empirical comparison of pruning methods for decision tree,' Machine Learning 4, pp. 319–342.
Mitchell, T.M. (1978), Version Space: an Approach to Concept Learning. Ph.D. Thesis, Stanford University.
Mitchell, T.M. (1982), 'Generalization as search,' Artificial Intelligence 18, pp. 203–226.
Quinlan, J.R. (1983), 'Learning efficient classification procedures and their application to chess end games,' in R.S. Michalski, J.G. Carbonell and T.M. Mitchell, eds., Machine Learning: An Artjficial Intelligence Approach, Vol. 1, Morgan Kaufmann, pp. 463–482.
Quinlan, J.R. (1986), 'The effect of noise on concept learning,' in R.S. Michalski, J.G. Carbonell and T.M. Mitchell, eds., Machine Learning: An Artificial Intelligence Approach, Vol. 2, Morgan Kaufmann, pp. 463–482.
Quinlan, J.R. (1986), 'Induction of decision trees,' Machine Learning 1(1), pp. 81–106.
Quinlan, J.R. (1987), 'Simplifying decision trees,' International Journal of Man-Machine Studies 27(4), pp. 221–234.
Quinlan, J.R. (1992), C4.5 Programs for Machine Learning. San Mateo, CA: Morgan Kaufmann.
Solso, R.L. (1988), Cognitive Psychology. MA: Allyn and Bacon.
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Hsu, YT., Hong, TP. & Tseng, SS. Learning Concepts by Arranging Appropriate Training Order. Minds and Machines 11, 399–415 (2001). https://doi.org/10.1023/A:1017599000794
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DOI: https://doi.org/10.1023/A:1017599000794