This Article 
 Bibliographic References 
 Add to: 
Generalized Version Space Learning Algorithm for Noisy and Uncertain Data
March-April 1997 (vol. 9 no. 2)
pp. 336-340

Abstract—This paper generalizes the learning strategy of version space to manage noisy and uncertain training data. A new learning algorithm is proposed that consists of two main phases: searching and pruning. The searching phase generates and collects possible candidates into a large set; the pruning phase then prunes this set according to various criteria to find a maximally consistent version space. When the training instances cannot completely be classified, the proposed learning algorithm can make a trade-off between including positive training instances and excluding negative ones according to the requirements of different application domains. Furthermore, suitable pruning parameters are chosen according to a given time limit, so the algorithm can also make a trade-off between time complexity and accuracy. The proposed learning algorithm is then a flexible and efficient induction method that makes the version space learning strategy more practical.

[1] G. Antoniou, "Version Space Algorithms on Hierarchies with Exceptions," Proc. Sixth Portuguese Conf. AI, pp. 136-149, 1993.
[2] B.G. Buchanan and E.H. Shortliffe, Rule-Based Expert Systems—The MYCIN Experiments of the Stanford Heuristic Programming Project.Reading, Mass.: Addison-Wesley, 1984.
[3] A. Bundy, B. Silver, and D. Plummer, "An Analytical Comparison of Some Rule-Learning Programs," Artificial Intelligence vol. 27, no. 2, pp. 137-181, 1985.
[4] C. Carpineto, "Shift of Bias without Operators," Proc. 10th European Conf. Artificial Intelligence, pp. 471-473, 1992.
[5] P. Clark and T. Niblett, "The CN2 Induction Algorithm," Machine Learning, vol. 3, pp. 261-283, 1989.
[6] L. De Raedt and M. Bruynooghe, "A Unifying Framework for Concept-Learning Algorithms," Knowledge Engineering Rev., vol. 7, no. 3, pp. 251-269, 1992.
[7] G. Drastal, R. Meunier, and S. Raatz, "Error Correction in Constructive Induction," Proc. Sixth Int'l Workshop Machine Learning, pp. 81-83, 1989.
[8] D. Haussler, “Quantifying Inductive Bias—AI Learning Algorithms and Valiant's Learning Framework,” Artificial Intelligence, vol. 36, pp. 177-221, 1988.
[9] H. Hirth, "Generalizing Version Space," Machine Learning, vol. 17, pp. 5-46, 1994.
[10] T.P. Hong, "A Study of Parallel Processing and Noise Management on Machine Learning," PhD thesis, National Chiao-Tung Univ., Taiwan, 1991.
[11] T.P. Hong and S.S. Tseng, "Splitting and Merging: An Approach to Disjunctive Concept Acquisition," Proc. IEEE Int'l Workshop Emerging Technologies and Factory Automation, pp. 201-205, 1992.
[12] Y. Kodratoff, M.V. Manago, and J. Blythe, "Generalization and Noise," Int'l J. Man Machine Studies, vol. 27, pp. 181-204, 1987.
[13] C.C. Lee, "Fuzzy Logic in Control Systems: Fuzzy Logic Controller, Parts I and II," IEEE Trans. Systems, Man, and Cybernetics, Vol. 20, No. 2, 1990, pp. 404-435.
[14] J. Mingers, “An Empirical Comparison of Pruning Methods for Decision Tree Induction,” Machine Learning, vol. 4, no. 2, pp. 227-243, 1989.
[15] T.M. Mitchell, “Version Spaces: An Approach to Concept Learning,” PhD thesis, Stanford Univ., 1978.
[16] T.M. Mitchell, "Generalization as Search," Artificial Intelligence, vol. 18, pp. 203-226, 1982.
[17] J. Nicolas, "Empirical Bias for Version Space," Proc. Int'l Joint Conf. AI, pp. 671-676, 1991.
[18] J.R. Quinlan, "The Effect of Noise on Concept Learning," Machine Learning: An Artificial Intelligence Approach, R.S. Michalski, J.G. Carbonell, and T.M. Mitchell, eds., vol. 2. Palo Alto, Calif.: Toiga, pp. 149-166, 1984.
[19] J.R. Quinlan,“Simplifying decision trees,” Int’l J. Man-Machine Studies, vol. 27, pp. 221-234, 1987.
[20] J.R. Quinlan, Unknown Attribute Values in Induction Proc. Sixth Int'l Conf. Machine Learning, 1989.
[21] J.R. Quinlan, C4.5: Programs for Machine Learning,San Mateo, Calif.: Morgan Kaufman, 1992.
[22] R.G. Reynolds and J.I. Maletic, "The Use of Version Space Controlled Genetic Algorithms to Solve the Boole Problem," Int'l J. Artificial Intelligence Tools, vol. 2, no. 2, pp. 219-234, 1993.
[23] E.N. Smirnov, "Space Fragmenting—A Method for Disjunctive Concept Acquisition," Proc. Fifth Int'l Conf. Artificial Intelligence: Methodology, Systems, and Application, pp. 97-104, 1992.
[24] P.E. Utgoff, Machine Learning of Inductive Bias.Boston: Kluwer, 1986.
[25] L.X. Wang and J. M. Mendel, "Generating Fuzzy Rules by Learning from Examples," Proc. IEEE Conf. Fuzzy Systems, pp. 203-210, 1992.

Index Terms:
Machine learning, version space, multiple version spaces, noise, uncertainty, training instance.
Tzung-Pei Hong, Shian-Shyong Tseng, "Generalized Version Space Learning Algorithm for Noisy and Uncertain Data," IEEE Transactions on Knowledge and Data Engineering, vol. 9, no. 2, pp. 336-340, March-April 1997, doi:10.1109/69.591457
Usage of this product signifies your acceptance of the Terms of Use.