Exploring the power of genetic search in learning symbolic classifiers

IEEE Transactions on Pattern Analysis and Machine Intelligence

Volumn 18, Issue 11, 1996, Pages 1135-1141

  Neri, Filippo ^a   Saitta, Lorenza ^a  

^a Uipartimento Ai Informatica   (Italy)

Author keywords

Classification rules; Disjunctive concept learning; Distributed genetic algorithms; Empirical comparison; Genetic algorithms; Machine learning; Mushroom dataset; Splice junctions dataset; Universal suffrage selection

Indexed keywords

CODES (SYMBOLS); GENETIC ALGORITHMS; LEARNING ALGORITHMS; LEARNING SYSTEMS; PROBLEM SOLVING;

LEARNING SYMBOLIC CLASSIFIERS; REGAL LEARNING SYMBOLIC CLASSIFIERS;

COMPUTER VISION;

EID: 0030286854     PISSN: 01628828     EISSN: None     Source Type: Journal    
DOI: 10.1109/34.544085     Document Type: Article

References (61)

1. C.A. Ankenbrandt, B.P- Buckles, and F.E- Petry, "Scene Recognition Using Genetic Algorithms with Semantic Nets," Pattern Recognition Letters, vol. 11, pp. 285-293, 1990.
2. J. Bala, K.A. De long, and P. Pachowicz, "Learning Noise Tolerant Classification Procedures by Integrating Inductive Learning and Genetic Algorithms" Proc. First Int'l Workshop on Muliistrategy Learning, Harpers Ferry, W. Va., pp. 316-323,1991.
3. P. Bonelli, A. Parodi, S. Sen, and S. Wilson, "NEWBOOLE: A Fast GBML System," Proc. Int'l Conf. Machine Learning, Austin, Tex., pp. 153-159,1990.
4. R. Boswell, "Manual for NewID Version 2.1," The Turing Inslitute, Glasgow, U.K., 1990.
5. M. Botta and A. Giordana, "Smart+: A Multi Strategy Learning 'lool," Proc. lath Int'l joint ConJ. Artificial Intelligence, Chambéry, France, pp. 937-943. 1993.
6. M. Botta, A, Giordana, and L. Saitta, "Learning Fuzzy Concept Definitions," Proc. Second IEEE Int'l Conf. Fuzzy Systems, San Francisco, Calif., pp. 18-22, 1993.
7. L. Breiman, J. Friedman, R. Olshen, and C. Stone, Classification and Regression Trees. Monterey, Calif.: Wadsworth, 1984.
8. P. Brèzellec and H. Soldano, "ÉLÉNA: A Bottom-Up Learning Method/' Proc. 10th Machine Learning ConJ., Amherst, Mass., pp. 9-16,1993.
9. F.Z. Brill, D.E. Brown, and W.N. Martin, "Fast Genetic Selection of Features for Neural Network Classifiers," IEEE Trans. Neural Network, vol. 3, no. 2, pp. 324-328,1992.
10. P. Clark and R. Boswell, "Rule Induction with CN2: Some Recent Improvements," Proc. FWSL-91, Porto, Portugal, pp. 151-163. 1991.
11. J.P. Cohoon, S.U. Hedge, W.N. Martin, and D. Richards, "Punctuated Equilibria: A Parallel Genetic Algorithm," Proc. Second Inl'l Conf. Genetic Algorithms, Cambridge, Mass., pp. 153-159, 1987.
12. S. Cost and S. Salzberg, "A Weighted Nearest Neighbour Algorithm for Learning with Symbolic Features," Machine Learning, vol, 10, pp. 57-78,1993.
13. T. Cover and D. Hart, "Nearest Neighbour Pattern Classification," IEEE Trans, information Theory, vol. 13, pp. 21-27,1967.
14. K. Deb and D. Coldbcrg, "An Investigation of Niches and Species Formation in Genetic Function Optimization," Proc. Third Int'l Conf. on Genetic Algorithms, Fairfax, Va., pp. 42-50,1989.
15. K.A. De Jong, "Analysis of the Behaviour of a Class of Genetic Adaptive Systems," Doctoral Dissertation, Dept. of Computer and Comm. Sciences, Univ. of Michigan, Ann Arbor, Mich., 1975.
16. K.A. De Jong, W.M. Spears, and F.D. Gordon, "Using Genetic Algorithms for Concept Learning/' Machine Learning, vol. 13, pp. 161-188,1993.
17. D.H. Fisher, "Knowledge Acquisition via Incremental Conceptual Clustering," Machine Learning, vol. 2, pp. 139-172,1987.
18. A. Giordana and C. Sale, "Genetic Algorithms for Learning Relations," Prac. Ninth Int'l Conf. Machine Learning, pp. 169-178, Aberdeen, U.K., 1992.
19. A. Ciordana and L. Saitta, "REGAL: An Integrated System for Learning Relations Using Genetic Algorithms," Proc. Second Int'l Workshop on Mullislralegy Learning, Harpers Ferry, W. Va., pp. 2,34-249,1993.
20. A. Giordana and L. Saitta, "Learning Disjunctive Concepts by Means of Genetic Algorithms," Proc. llth Int'l Conf. Machine Learning, New Brunswick, N.J., pp. 96-104,1994.
21. A. Giordana, G. Lo Bello, and L. Saitta, "ion in Prepositional Calculus," Proc. Workshop Knowledge Compilation und Speed Lip Learning, Amherst, Mass., pp. 56-64,1994.
22. A. Giordana and F. Neri, "Search-Intensive Concept Learning," Evolutionary Computation, MIT Press, vol. 3, no. 4, pp. 375-496, 1996.
23. D.E. Goldberg and J. Richardson, "Genetic Algorithms with Sharing for Multimodal Function Optimization," Proc. Second Int'l Conf. on Genetic Algorithms, Cambridge, Mass., pp. 41-49,1987.
24. D.E. Goldberg, Genetic Algorithms. Addison-Wesley, 1989.
25. D.P. Greene and S.F. Smith, "Competition-Based Induction of Decision Models from Examples," Machine Learning, vol. 1.3, pp. 229-258,1993.
26. J. Hekanaho, "Symbiosis in Multimodal Concept Learning," Proc. 12lli Int'l Conf. Machine Learning, Lake Tahoe, Calif:, Morgan Kaufmann, pp. 278-285,1995.
27. J.H. Holland, "Adaptation in Natural and Artificial Systems," PhD Thesis, Univ. of Michigan, Ann Arbor, Mich.
28. J.H. Holland, "Escaping Brittleness: The Possibilities of General Purpose Learning Algorithms Applied to Parallel Rule-Based Systems," R. Michalski, J. Carbonell, and T. Mitchell, eds., Machine Learning: An At Approach, vol. II. Los Altos, Calif.: Morgan Kaufmann, pp. 593-623,1986.
29. R.C. Holte, "Very Simple Classification Rules Perform Well on Most Commonly Used Datasets," Machine Learning, vol. 11, pp. 63-90, 1993.
30. J. Horn, K. Deb, and D.E. Goldberg, "Implicit Niching in a Learning Classifier System: Nature's Way," Evolutionary Computation, vol. 2, pp. 37'-66,1994.
31. C.Z. Janikow, "A Knowledge Intensive Genetic Algorithm for Supervised Learning" Machine Learning, vol. 13, pp. 198-228,1993.
32. M. Koppel, A.M. Segre, and R. Feldman, "Getting the Most from Flawed Theories," Proc. llth Int'l Conf. Machine Learning, New Brunswick, N.J., pp. 139-147, 1994.
33. B. Lewin, Gene V. New York: Oxford Univ. Press, 1994.
34. S.W. Mahfoud, "Niching Methods for Genetic Algorithms/' PhD Thesis, Univ. of Illinois, Urbana-Champaign, 111. 1995.
35. R.A. McCallum and K.A. Spackman. "Using Genetic Algorithm to Learn Disjunctive Rules from Examples," Proc. Seventh Int'l Conf. on Machine learning, Austin, Tex., pp. 149-152,1990.
36. R. Michalski, "A Theory and Methodology of Inductive Learning," R. Michalski, J. Carbonell, and T. Mitchell, eds., Machine Learning: An M Approach, vol. L Morgan Kaufmann, Los Altos, Calif., pp. 83-134.1983.
37. R. Michalski, I. Mozetic, J. Hong, and N. Lavrac, "The Multipurpose Incremental Learning System AQ15 and its Testing Application to Three Medical Domains," Proc. Fifth Nat'1 Conf. Artificial Intelligence, Philadelphia, Penn., pp. 1,041-1,045,1986.
38. F. Neri and L. Saitta, "Analysis of Genetic Algorithms Evolution under Pure Selection." Proc.Sixth Int'l Conf. Genetic Algorithms, Pittsburgh, Penn., pp. 32-41.1995.
39. F. Neri and A. Giordana, "A Parallel Genetic Algorithm for Concept Learning," Proc. Sixth Int'l Conf. Genetic Algorithms, Pittsburgh, Penn., pp. 436-443,1995.
40. M. Noordewier, G. Towell, and J. Shavlik, "Training KnowledgeBased Neural Networks to Recognize Genes in DNA Sequences," WPS, vol 3, Denver, Col., 1991.
41. S.W. Norton and H. Hirsh, "Learning DNF via Probabilistic Evidence Combination," Proc. 10th Int'l Conf. Machine Learning, Amherst, Mass., pp. 220-227,1993.
42. D.W. Opitz and J.W. Shavlik, "Using Genetic Search to Refine Knowledge-Based Neural Networks," Proc. llth Int'l Conf. Machine Learning, New Brunswick, N.J., pp. 208-216.
43. J.R. Quintan, "Induction of Decision Trees," Machine Learning, vol. 1, pp. 81-106,1986.
44. J.R. Quinlan, "Learning Logical Definitions from Relations," Machine Learning, vol. 5, pp. 239-266,1990.
45. J. Rachlin, S. Kasif, S. Salzberg, and D.W. Aha, "Towards a Better Understanding of Memory-Based Reasoning Systems," Proc. nth Conf. Machine Learning, New Brunswick, N.J., pp. 242-250, 1994.
46. F. Rosenblatt, "Principles of Neurodynamics: Pcrccptrons and the Theory of Brain Mechanisms," New York: Spartan, 1962.
47. D.E. Rumelhart and J.L. McClelland, "Parallel Distributed Processing: Exploration in the Microstructure of Cognition," PDF Research Group eds., Cambridge, Mass.: MIT Press, 1986.
48. D.E. Rumelhart, G.E. Hinton, and RJ. Williams, "Learning Internal Representation by Error Propagation," D.E. Rumelhart, J.L. McClelland, and the POP Research Group, eds., Parallel Distributed Processing: Exploration in the Microstructure uf Cognition, vol. I, Cambridge, Mass.: MIT Press, pp. 318-363,1986.
49. J.S. Schlimmer, "Concept Acquisition through Representational Adjustment," TR 87-19, Dept. of Information and Computer Science, Univ. of California, Irvine, 1987.
50. W. Siedlecki and J. Sklanski, "A Note on Genetic Algorithms for Large Scale Feature Selection," Pattern Recognition Letters, vol. 10, pp. 335-347,1989.
51. S. Smith, "Flexible Learning of Problem Solving Heuristics through Adaptive Search," Proc. Eighth Int'l Joint Conf. Artificial Intelligence, Karlsruhe, Germany, pp. 422-425, 1983.
52. W.M. Spears, "Simple Subpopulation Schemes," Proc. Conf. Evolutionary Programming,. San Diego, Calif., pp. 296-307,1994.
53. R.S. Button, "Learning to Predict by the Methods of Temporal Differences," Machine Learning, vol. 3, pp. 9-44,1988.
54. G. Syswerda, "Uniform Crossover in Genetic Algorithms," Proc. Third Int'l Conf. Genetic Algorithms, Fairfax, Va., pp. 2-9,1989.
55. G. Towell and J. Shavlik, "Refining Symbolic Knowledge Using Neural Networks," Proc. First Workshop Multistrategy Learning, Harpers Ferry, W. Va., pp. 257-272,1991.
56. G.G. 1 owell and J.W. Shavlik, "Knowledge-Based Artificial Neural Networks," Artificial Intelligence, vol. 70, pp. 119-165,1994.
57. H. Vafaie and K.A. De Jong, "Improving the Performance of Rule Induction System Using Genetic Algorithms," Proc. First Int'l Workshop on Multistrategy Learning, Harpers Ferry, Va., pp. 305-315,1991.
58. G. Venturing "SIA: A Supervised Inductive Algorithm with Genetic Search for Learning Attribute Based Concepts," Proc. Furapean Conf. Machine Learning, Vienna, Austria, pp. 280-296.1993.
59. S.M. Weiss and N. Indurkhya, "Small Sample Decision Tree Pruning," Proc. llth Machine Learning Conf., New Brunswick, N.J., pp. 335-342,1994,
60. D.Y. Yeung, "A Neural Network Approach to Constructive Induction," Proc. Eighth Int'l Conf. on Machine Learning, Evanston, 111., pp. 228-232,1991.
61. S. Wilson, "Classifier Systems and the Animat Problem," Machine Learning, vol. 2, 199-228,1987.