An empirical comparison of supervised learning algorithms

ICML 2006 - Proceedings of the 23rd International Conference on Machine Learning

Volumn 2006, Issue , 2006, Pages 161-168

  Caruana, Rich ^a   Niculescu Mizil, Alexandru ^a  

^a Department of Computer Science and School of Operations Research and Information Engineering   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CALIBRATION; DECISION THEORY; LARGE SCALE SYSTEMS; LEARNING SYSTEMS; NEURAL NETWORKS; PROJECT MANAGEMENT; REGRESSION ANALYSIS; TREES (MATHEMATICS); VECTORS;

COMPREHENSIVE EMPIRICAL EVALUATION; LOGISTIC REGRESSION; MEMORY BASED LEARNING; SUPERVISED LEARNING METHODS;

LEARNING ALGORITHMS;

EID: 33749254096     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (25)

1. Ayer, M., Brunk, H., Ewing, G., Reid, W., & Silverman, E. (1955). An empirical distribution function for sampling with incomplete information. Annals of Mathematical Statistics, 5, 641-647.
2. Bauer, E., & Kohavi, R. (1999). An empirical comparison of voting classification algorithms: Bagging, boosting, and variants. Machine Learning, 36.
3. Blake, C., & Merz, C. (1998). UCI repository of machine learning databases.
4. Breiman, L. (1996). Bagging predictors. Machine Learning, 24, 123-140.
5. Breiman, L. (2001). Random forests. Machine Learning, 45, 5-32.
6. Buntine, W., & Caruana, R. (1991). Introduction to ind and recursive partitioning (Technical Report FIA-91-28). NASA Ames Research Center.
7. Caruana, R., & Niculescu-Mizil, A. (2004). Data mining in metric space: An empirical analysis of suppervised learning performance criteria. Knowledge Discovery and Data Mining (KDD'04).
8. Cooper, G. F., Aliferis, C. F., Ambrosino, R., Aronis, J., Buchanan, B. G., Caruana, R., Fine, M. J., Glymour, C., Gordon, G., Hanusa, B. H., Janosky, J. E., Meek, C., Mitchell, T., Richardson, T., & Spirtes, P. (1997). An evaluation of machine learning methods for predicting pneumonia mortality. Artificial Intelligence in Medicine, 9.
9. Giudici, P. (2003). Applied data mining. New York: John Wiley and Sons.
10. Gualtieri, A., Chettri, S. R., Cromp, R., & Johnson, L. (1999). Support vector machine classifiers as applied to aviris data. Proc. Eighth JPL Airborne Geoscience Workshop.
11. Joachims, T. (1999). Making large-scale svm learning practical. Advances in Kernel Methods.
12. King, R., Feng, C., & Shutherland, A. (1995). Statlog: comparison of classification algorithms on large real-world problems. Applied Artificial Intelligence, 9.
13. LeCun, Y., Jackel, L. D., Bottou, L., Brunot, A., Cortes, C., Denker, J. S., Drucker, H., Guyon, I., Muller, U. A., Sackinger, E., Simard, P., & Vapnik, V. (1995). Comparison of learning algorithms for handwritten digit recognition. International Conference on Artificial Neural Networks (pp. 53-60). Paris: EC2 & Cie.
14. Lim, T.-S., Loh, W.-Y., & Shih, Y.-S. (2000). A comparison of prediction accuracy, complexity, and training time of thirty-three old and new classification algorithms. Machine Learning, 40, 203-228.
15. Niculescu-Mizil, A., & Caruana, R. (2005). Predicting good probabilities with supervised learning. Proc. 22nd International Conference on Machine Learning (ICML'05).
16. Perlich, C., Provost, F., & Simonoff, J. S. (2003). Tree induction vs. logistic regression: a learning-curve analysis. J. Mach. Learn. Res., 4, 211-255.
17. Platt, J. (1999). Probabilistic outputs for support vector machines and comparison to regularized likelihood methods. Adv. in Large Margin Classifiers.
18. Provost, F., & Domingos, P. (2003). Tree induction for probability-based rankings. Machine Learning.
19. Provost, F. J., & Fawcett, T. (1997). Analysis and visualization of classifier performance: Comparison under imprecise class and cost distributions. Knowledge Discovery and Data Mining (pp. 43-48).
20. Robertson, T., Wright, F., & Dykstra, R. (1988). Order restricted statistical inference. New York: John Wiley and Sons.
21. Schapire, R. (2001). The boosting approach to machine learning: An overview. In MSRI Workshop on Nonlinear Estimation and Classification.
22. Vapnik, V. (1998). Statistical learning theory. New York: John Wiley and Sons.
23. Witten, I. H., & Frank, E. (2005). Data mining: Practical machine learning tools and techniques. San Francisco: Morgan Kaufmann. Second edition.
24. Zadrozny, B., & Elkan, C. (2001). Obtaining calibrated probability estimates from decision trees and naive bayesian classifiers. ICML.
25. Zadrozny, B., & Elkan, C. (2002). Transforming classifier scores into accurate multiclass probability estimates. KDD.