Ranking-based evaluation of regression models

Knowledge and Information Systems

Volumn 12, Issue 3, 2007, Pages 331-353

  Rosset, Saharon ^a   Perlich, Claudia ^a   Zadrozny, Bianca ^b  

^a IBM T J WATSON RESEARCH CENTER   (United States)

^b FLUMINENSE FEDERAL UNIVERSITY   (Brazil)

Author keywords

Evaluation robustness; Model evaluation; Performance visualization; Ranking correlation; Regression

Indexed keywords

REGRESSION ANALYSIS;

AREA UNDER THE CURVES; CORRELATION COEFFICIENT; GRAPHICAL REPRESENTATIONS; MODEL EVALUATION; PERFORMANCE VISUALIZATION; RANKING CORRELATION; REGRESSION; SIZE ESTIMATION MODELS;

LEARNING TO RANK;

EID: 34547732000     PISSN: 02191377     EISSN: 02193116     Source Type: Journal    
DOI: 10.1007/s10115-006-0037-3     Document Type: Article

References (13)

1. Bay SD (2000) UCI KDD archive. Department of Information and Computer Sciences, University of California, Irvine. Available at: http://kdd.ics.uci.edu/
2. Bi J, Bennett KP (2003) Regression error characteristic curves. In: Proceedings of the twentieth international conference on machine learning (ICML-03), Washington, DC
3. Bradley AP (1997) The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recog 30(7):1145-1159
4. Egan J (1975) Signal detection theory and ROC analysis. Academic Press, San Diego
5. Garland R (2004) Share of wallet's role in customer profitability. J Finan Serv Mark 8(3):259-268
6. Georges J, Milley AH (1999) KDD'99 competition: Knowledge discovery contest report. Available at: http://www-cse.ucsd.edu/users/elkan/ kdresults.html
7. Hampel FR, Ronchetti EM, Rousseeuw PJ, Stahel WA (1986) Robust statistics: The approach based on influence functions. Wiley, New York
8. Kendall M, Gibbons JM (1990) Rank correlation methods. Edward Arnold, London
9. Noether NE (1967) Elements of nonparametric statistics. Wiley, New York
10. Vapnik V (1995) The nature of statistical learning theory. Springer-Verlag, Berlin Heidelberg New York
11. Vucetic S, Obradovic Z (2005) Collaborative filtering using a regression-based approach. Knowl Inf Syst 7(1)
12. Witten IH, Frank E (2000) Data mining: Practical machine learning tools with Java implementations. Morgan Kaufmann, San Francisco. Available at: http://www.cs.waikato.ac.nz/ml/weka/
13. Wu X, Yu P, Piatetsky-Shapiro G, Cercone N, Lin TY, Kotagiri R, Wah BW (2003) Data mining: How research meets practical development? Knowl Inf Syst 5(2):248-261