A cost sensitive technique for ordinal classification problems

Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)

Volumn 3025, Issue , 2004, Pages 220-229

  Kotsiantis, Sotiris B ^a   Pintelas, Panagiotis E ^a  

^a UNIVERSITY OF PATRAS   (Greece)

Author keywords

[No Author keywords available]

Indexed keywords

COSTS; LEARNING SYSTEMS; OPTIMIZATION; REGRESSION ANALYSIS; SENSITIVITY ANALYSIS; SET THEORY; ARTIFICIAL INTELLIGENCE;

COST SENSITIVE; ORDINAL CLASSIFICATION; PREDICTED CLASSES; STANDARD REGRESSION; CLASSIFICATION ACCURACY; COST-SENSITIVE; MISCLASSIFICATION COSTS; REAL-WORLD; ROBUST SOLUTIONS; SYSTEMATIC STUDY;

PROBLEM SOLVING; COSTS;

EID: 9444235102     PISSN: 03029743     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/978-3-540-24674-9_24     Document Type: Conference Paper

References (14)

1. Aha, D.: Lazy Learning. Dordrecht: Kluwer Academic Publishers (1997).
2. Atkeson, C. G., Moore, A.W., & Schaal, S.: Locally weighted learning. Artificial Intelligence Review 11 (1997) 11-73.
3. Allwein, E. L., Schapire, R. E., and Singer, Y.: Reducing multiclass to binary: A unifying approach for margin classifiers. Journal of Machine Learning Research 1 (2000) 113-141.
4. Blake, C.L. & Merz, C.J.: 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] (1998).
5. Frank, E. and Hall M.: A simple approach to ordinal prediction, L. De Raedt and P. Flach (Eds.): ECML 2001, LNAI 2167, pp. 145-156, 2001, Springer-Verlag Berlin.
6. Frank E. and Witten I.: Generating Accurate Rule Sets Without Global Optimization. In Shavlik, J., ed., Machine Learning: Proceedings of the Fifteenth International Conference (1998), Morgan Kaufmann Publishers, San Francisco, CA.
7. Herbrich R., Graepel T., and Obermayer K.: Regression models for ordinal data: A machine learning approach. Technical report, TU Berlin, 1999.
8. S. Kramer, G. Widmer, B. Pfahringer, and M. DeGroeve. Prediction of ordinal classes using regression trees. Fundamenta Informaticae (2001).
9. Murthy (1998), Automatic Construction of Decision Trees from Data: A Multi-Disciplinary Survey, Data Mining and Knowledge Discovery 2 (1998) 345-389, Kluwer Academic Publishers.
10. Potharst R. and Bioch J.C.: Decision trees for ordinal classification. Intelligent Data Analysis 4(2000) 97-112.
11. Quinlan J.R.: C4.5: Programs for machine learning. Morgan Kaufmann, San Francisco (1993).
12. Wang, Y. & Witten, I. H.: Induction of model trees for predicting continuous classes, In Proc. of the Poster Papers of the European Conference on ML, Prague (1997) 128-137. Prague: University of Economics, Faculty of Informatics and Statistics.
13. Wilson, D., Martinez, T.: Reduction Techniques for Instance-Based Learning Algorithms Machine Learning 38 (2000) 257-286, Kluwer Academic Publishers.
14. Witten I. & Frank E.: Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations, Morgan Kaufmann, San Mateo (2000).