Nomograms for visualization of naive Bayesian classifier

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3202, Issue , 2004, Pages 337-348

  Možina, Martin ^a   Demšar, Janez ^a   Kattan, Michael ^b   Zupan, Blaž ^a,c  

^a UNIVERSITY OF LJUBLJANA   (Slovenia)

^b MEMORIAL SLOAN KETTERING CANCER CENTER   (United States)

^c BAYLOR COLLEGE OF MEDICINE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN NETWORKS; DECISION SUPPORT SYSTEMS; NOMOGRAMS; PROBABILITY; VISUALIZATION;

ATTRIBUTE VALUES; CLASS PROBABILITIES; CONFIDENCE INTERVAL; DECISION SUPPORTS; NAIVE BAYESIAN; NAIVE BAYESIAN CLASSIFIER; PREDICTIVE PERFORMANCE; TRAINING DATA;

CLASSIFICATION (OF INFORMATION);

EID: 33750710231     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-30116-5_32     Document Type: Article

References (15)

1. Domingos, P., Pazzani, M.: Beyond independence: conditions for the optimality of the simple Bayesian classifier. In: Proceedings of the Thirteenth International Conference on Machine Learning, Bari, Italy, Morgan Kaufmann (1996) 105-112
2. Kononenko, I.: Inductive and bayesian learning in medical diagnosis. Applied Artificial Intelligence 7 (1993) 317-337
3. Becker, B., Kohavi, R., Sommerfield, D.: Visualizing the simple Bayesian classifier. In Fayyad, U., Grinstein, G., Wierse, A., eds.: Information Visualization in Data Mining and Knowledge Discovery. Morgan Kaufmann Publishers, San Francisco (2001) 237-249
4. Lubsen, J., Pool, J., van der Does, E.: A practical device for the application of a diagnostic or prognostic function. Methods of Information in Medicine 17 (1978) 127-129
5. Harrell, F.E.: Regression modeling strategies: with applications to linear models, logistic regression, and survival analysis. Springer, New York (2001)
6. Kattan, M.W., Eastham, J.A., Stapleton, A.M., Wheeler, T.M., Scardino, P.T.: A preoperative nomogram for disease recurrence following radical prostatectomy for prostate cancer. J Natl Cancer Inst 90 (1998) 766-71
7. Hosmer, D.W., Lemeshow, S.: Applied Logistic Regression. John Wiley & Sons, New York (2000)
8. Cestnik, B.: Estimating probabilities: A crucial task in machine learning. In: Proceedings of the Ninth European Conference on Artificial Intelligence. (1990) 147-149
9. Demšar, J., Zupan, B.: Orange: From experimental machine learning to interactive data mining. White Paper [http://www.ailab.si/orange], Faculty of Computer and Information Science, University of Ljubljana (2004)
10. Zupan, B., Demšar, J., Kattan, M.W., Beck, J.R., Bratko, I.: Machine learning for survival analysis: A case study on recurrence of prostate cancer. Artificial Intelligence in Medicine 20 (2000) 59-75
11. Tufte, E.R.: The visual display of quantitative information. Graphics Press, Cheshire, Connecticut (1983)
12. Ware, C.: Information Visualization: Perception for Design. Morgan Kaufmann Publishers (2000)
13. Jakulin, A., Bratko, I.: Analyzing attribute dependencies. In: Proc. of the 7th European Conference on Principles and Practice of Knowledge Discovery in Databases, Dubrovnik (2003) 229-240
14. ∼mlearn/mlrepository.html]. Irvine, CA: University of California, Department of Information and Computer Science (1994)
15. Ng, A., Jordan, M.: On discriminative vs. generative classifiers: A comparison of logistic regression and naive bayes. In: Proc. of Neural Information Processing Systems). Volume 15. (2003)