Local boosting of decision stumps for regression and classification problems

Journal of Computers

Volumn 1, Issue 4, 2006, Pages 30-37

  Kotsiantis, S B ^a   Kanellopoulos, D ^a   Pintelas, P E ^a  

^a UNIVERSITY OF PATRAS   (Greece)

Author keywords

Classifier; Data mining; Machine learning; Regressor

Indexed keywords

CLASSIFICATION (OF INFORMATION); CLASSIFIERS; DATA MINING;

BENCHMARK DATASETS; BOOSTING APPROACH; COMBINING METHOD; DATA CHARACTERISTICS; DATA MINING PROBLEMS; HETEROGENEOUS DATASETS; PREDICTION MODEL; REGRESSOR;

LEARNING SYSTEMS;

EID: 55749108162     PISSN: 1796203X     EISSN: None     Source Type: Journal    
DOI: 10.4304/jcp.1.4.30-37     Document Type: Article

References (25)

1. D. Aha, Lazy Learning. Dordrecht: Kluwer Academic Publishers, 1997.
2. C. G. Atkeson, A. W. Moore, and S. Schaal, Locally weighted learning for control. Artificial Intelligence Review, 11:75-113, 1997.
3. C.G. Atkeson, A.W. Moore & S. Schaal, Locally weighted learning. Artificial Intelligence Review, 11(1-5), 11-73, 1997.
4. C. Blake & C. Merz, UCI Repository of machine learning databases. Irvine, CA: University of California, Department of Information and Computer Science. http://www.ics.uci.edu/mlearn/MLRepository.html
5. E. Bauer and R. Kohavi. An empirical comparison of voting classification algorithms: Bagging, boosting and variants. Machine Learning, 36(1/2):525-536, 1999.
6. H. Brighton, C. Mellish, Advances in Instance Selection for Instance-Based Learning Algorithms, Data Mining and Knowledge Discovery, 6, 153-172, 2002.
7. L. Bottou and V. Vapnik, Local learning algorithm, Neural Computation, vol. 4, no. 6, pp. 888-901, 1992.
8. L. Breiman, Bagging Predictors. Machine Learning, 24(3), 123-140.Kluwer Academic Publishers, 1996.
9. S. Cohen and N. Intrator. Automatic Model Selection in a Hybrid Perceptron/ Radial Network. In Multiple Classifier Systems. 2nd Inter. Workshop, MCS 2001, pages 349-358.
10. Duffy, N. Helmbold, D., Boosting Methods for Regression, Machine Learning, 47, (2002) 153-200.
11. C. John and L. Trigg, K*: An Instance- based Learner Using an Entropic Distance Measure, Proc. of the 12th International Conference on ML, pp. 108-114, 1995.
12. E. Frank, M. Hall and B. Pfahringer, Locally weighted naive Bayes. Proc. of the 19th Conference on Uncertainty in Artificial Intelligence. Mexico, 2003.
13. Y. Freund and R. Schapire, Experiments with a New Boosting Algorithm, Proc. ICML'96, p. 148-156, 1996
14. Friedman J., Stochastic Gradient Boosting, Computational Statistics and Data Analysis 38 (2002) 367-378.
15. W. Iba, & P. Langley, Induction of one-level decision trees. Proc. of the Ninth Inter. Machine Learning Conference (1992). Scotland: Morgan Kaufmann.
16. E.M. Kleinberg. A Mathematically Rigorous Foundation for Supervised Learning, volume 1857 of Lecture Notes in Computer Science, pages 67-76. Springer-Verlag, 2000.
17. Kohavi R., The Power of Decision Tables. In Proc European Conference on ML (1995) 174-189.
18. T. Lazarevic, Z. Obradovic, Adaptive Boosting for Spatial Functions with Unstable Driving Attributes, In Proc. of Pacific-Asia Conf. on Knowledge Discovery and Data Mining, 2000, pp. 329-340.
19. Loader, C., Local Regression and Likelihood. Springer, New York, (1999).
20. P. Melville and R. Mooney, Constructing Diverse Classifier Ensembles using Artificial Training Examples, Proc. of the IJCAI-2003, pp.505-510, Mexico, 2003
21. Nadeau, C., Bengio, Y., Inference for the Generalization Error. Machine Learning, 52 (2003) 239-281.
22. V.N. Vapnik, Statistical Learning Theory, Wiley, New York, 1998.
23. G. I. Webb, MultiBoosting: A Technique for Combining Boosting and Wagging, Machine Learning, 40, 159-196, Kluwer Academic Publishers, 2000.
24. D. Wilson, and T. Martinez, Reduction Techniques for Instance-Based Learning Algorithms, Machine Learning, 38, 257-286, 2000.
25. I. Witten, and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann, San Mateo, CA, 2000.