Specific-class distance measures for nominal attributes

AI Communications

Volumn 26, Issue 3, 2013, Pages 261-279

  El Hindi, Khalil ^a  

^a KING SAUD UNIVERSITY   (Saudi Arabia)

Author keywords

distance functions; instance based learning; kNN algorithm; lazy learners; Machine learning; similarity metrics

Indexed keywords

DISTANCE FUNCTIONS; INSTANCE BASED LEARNING; K-NN ALGORITHM; LAZY LEARNERS; SIMILARITY METRICS;

LEARNING SYSTEMS;

CLASSIFICATION (OF INFORMATION);

EID: 84884568930     PISSN: 09217126     EISSN: None     Source Type: Journal    
DOI: 10.3233/AIC-130565     Document Type: Article

References (25)

1. D.W. Aha, D. Kibler and M.K. Albert, Instance-based learning algorithms, Machine Learning 6(1) (1991), 37-66. (Pubitemid 21727227)
2. A. Argentini and E. Blanzieri, Neighborhood counting measure metric and minimum risk metric: An empirical comparison, Information Engineering and Computer Science, Technical Report DISI-08-057, Univ. of Toronto.
3. C. Blake and C. Merz, UCI repository of machine learning databases [online], Department of Information and Computer Science, University of California, Irvine, CA, 1998, available at: http://www.ics.uci.edu/~mlearn/ MLRepository.html.
4. E. Blanzieri and F. Ricci, Probability based metrics for nearest neighbor classification and case-based reasoning, Lecture Notes in Computer Science 1650 (1999), 14-29.
5. M. Boullé, Modl: A Bayes optimal discretization method for continuous attributes, Machine Learning 65(1) (2006), 131-165. (Pubitemid 44451196)
6. M.D. Buhmann and M.D. Buhmann, Radial Basis Functions, Cambridge Univ. Press, New York, NY, USA, 2003.
7. J. Dougherty, R. Kohavi and M. Sahami, Supervised and unsupervised discretization of continuous features, in: Proceedings of the Twelfth International Conference on Machine Learning, Morgan Kaufmann Publishers, 1995, pp. 194-202.
8. K. El Hindi and M. Al-Akhras, Eliminating border instance to avoid overfitting, in: International Conference Intelligent Systems and Agents, Algarve, Portugal, 2009.
9. K. El Hindi andM. Al-Akhras, Smoothing decision boundaries to avoid overfitting in neural network training, Neural Network World 21 (2011), 311-325.
10. U. Fayyad and K. Irani, Multi-interval discretization of continuous values attributes for classification learning, in: Proceedings of 13th International Joint Conference on Artificial Intelligence, 1993.
11. A. Globerson and S. Roweis, Metric learning by collapsing classes, Advances in Neural Information Processing Systems 18 (2006), 451.
12. T. Kohonen, M.R. Schroeder and T.S. Huang, eds, Self- Organizing Maps, 3rd edn, Springer-Verlag, New York, Secaucus, NJ, USA, 2001.
13. S.P. Lloyd, Least square quantization in pcm, IEEE Transaction on Information Theory 28(2) (1982), 129-137.
14. T.M. Mitchell, Machine Learning, McGraw-Hill, New York, NY, USA, 1997.
15. J.R. Quinlan, C4.5: Programs for Machine Learning, Morgan Kaufmann, 1993.
16. C. Schaffer, A conservation law for generalization performance, in: The Eleventh International Conference on Machine Learning, 1994.
17. C. Stanfill and D. Waltz, Toward memory-based reasoning, Communication of ACM 9 (1986), 1213-1228.
18. H. Wang, Nearest neighbors by neighborhood counting, IEEE Trans. Pattern Analysis and Machine Intelligence 28 (2006), 942-953.
19. H. Wang, Neighborhood counting measure and minimum risk metric, IEEE Trans. Pattern Analysis and Machine Intelligence 32 (2010), 766-768.
20. H. Wang and W. Dubitzky, A flexible and robust similarity measure based on contextual probability, in: International Joint Conference on Artificial Intelligence, Vol. 19, Lawrence Erlbaum Associates, 2005, p. 27.
21. K.Q. Weinberge and L.K. Saul, Distance metric learning for large margin, Journal of Machine Learning Research 10 (2009), 207-244.
22. D.L. Wilson, Asymptotic properties of nearest neighbor rules using edited data, IEEE Transactions on Systems, Man, and Cybernetics 2(3) (1972), 408-421.
23. D.L. Wilson, An experiment with the edited nearest-neighbor rule, IEEE Transactions on Systems, Man, and Cybernetics 6(6) (1976), 448-452.
24. D.R. Wilson and T.R. Martinez, Improved heterogeneous distance functions, 1997, arXiv preprint cs/9701101.
25. I. Witten and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann, 2005.