Explanation and reliability of prediction models: The case of breast cancer recurrence

Knowledge and Information Systems

Volumn 24, Issue 2, 2010, Pages 305-324

  Štrumbelj, Erik ^a   Bosnić, Zoran ^a   Kononenko, Igor ^a   Zakotnik, Branko ^b   Kuhar, Cvetka Grašič ^b  

^a UNIVERSITY OF LJUBLJANA   (Slovenia)

^b INSTITUTE OF ONCOLOGY   (Slovenia)

Author keywords

Breast cancer; Classification explanation; Data mining; Machine learning; Prediction reliability

Indexed keywords

CLASSIFICATION (OF INFORMATION); DATA MINING; DECISION MAKING; DISEASES; FORECASTING; LEARNING SYSTEMS; MACHINE LEARNING; RELIABILITY;

BREAST CANCER; DATA SET; DECISION MAKING PROCESS; EXPERT USERS; MACHINE LEARNING MODELS; PREDICTION MODEL; REGRESSION PREDICTIONS; RELIABILITY ESTIMATION;

PREDICTIVE ANALYTICS;

EID: 77954953206     PISSN: 02191377     EISSN: 02193116     Source Type: Journal    
DOI: 10.1007/s10115-009-0244-9     Document Type: Article

References (30)

1. Andrews R, Diederich J, Tickle AB (1996) Survey and critique of techniques for extracting rules from trained artificial neural networks. Knowl Based Syst 8: 373-389.
2. Becker B, Kohavi R, Sommerfield D (1997) Visualizing the simple bayesian classier. In: KDD Workshop on issues in the integration of data mining and data visualization.
3. Bosnić Z, Kononenko I (2007) Estimation of individual prediction reliability using the local sensitivity analysis. Appl Intell 29(3): 187-203.
4. Bosnić Z, Kononenko I (2008) Automatic selection of reliability estimates for individual predictions. Knowl Eng Rev (in press).
5. Bosnić Z, Kononenko I (2008) Comparison of approaches for estimating reliability of individual regression predictions. Data Knowl Eng 67(3): 504-516.
6. Bosnić Z, Kononenko I (2009) An overview of advances in reliability estimation of individual predictions in machine learning. Intell Data Anal 13(2): 385-401.
7. Breiman L (2001) Random forests. Mach Learn J 45: 5-32.
8. Craven MW, Shavlik J (1994) Using sampling and queries to extract rules from trained neural networks. In: Proceedings of international conference on machine learning, pp 37-45.
9. Crowder MJ, Kimber A, Smith RL, Sweeting T (1991) Statistical concepts in reliability. Statistical analysis of reliability data. Chapman & Hall, London.
10. De Falco I, Della Cioppa A, Iazzetta A, Tarantino E (2006) An evolutionary approach for automatically extracting intelligible classification rules. Knowl Inf Syst 7: 179-201.
11. de Santana AL, Frances C, Rocha CA, Carvalho SV, Vijaykumar NL, Rego LP, Costa JC (2007) Strategies for improving the modeling and interpretability of bayesian networks. Data Knowl Eng 63(1): 91-107.
12. Gammerman A, Vovk V, Vapnik V (1998) Learning by transduction. In: Proceedings of the 14th conference on uncertainty in artificial intelligence. Madison, Wisconsin, pp 148-155.
13. Guyon I, Elisseeff A (2003) An introduction to variable and feature selection. J Mach Learn Res 3: 1157-1182.
14. Hamel L (2006) Visualization of support vector machines with unsupervised learning. In: Proceedings of 2006 IEEE symposium on computational intelligence in bioinformatics and computational biology. pp 1-8.
15. Jakulin A, Možina M, Demšar J, Bratko I, Zupan B (2005) Nomograms for visualizing support vector machines. In: KDD '05: proceeding of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining. pp 108-117.
16. Kohavi R, John G (1997) Wrappers for feature subset selection. Artif Intell J 97(1-2): 273-324.
17. Kononenko I (1993) Inductive and bayesian learning in medical diagnosis. Appl Artif Intell 7: 317-337.
18. Kononenko I (2001) Machine learning for medical diagnosis: history, state of the art and perspective. Artif Intell Med 23: 89-109.
19. Kononenko I, Kukar M (2007) Machine learning and data mining: introduction to principles and algorithms. Horwood, New York.
20. Kukar M (2004) Quality assessment of individual classifications in machine learning and data mining. Knowl Inf Syst 9(3): 364-384.
21. Lemaire V, Féraud R, Voisine N (2008) Contact personalization using a score understanding method. In: International joint conference on neural networks (IJCNN).
22. Liu H, Yu L (2005) Toward integrating feature selection algorithms for classification and clustering. Knowl Data Eng 17(4): 491-502.
23. Možina M, Demšar J, Kattan M, Zupan B (2004) Nomograms for visualization of naive bayesian classifier. In: PKDD '04: proceedings of the 8th European conference on principles and practice of knowledge discovery in databases, New York, NY, USA, 2004. Springer, New York, pp 337-348.
24. Poulet F (2004) Svm and graphical algorithms: a cooperative approach. In: Proceedings of fourth IEEE international conference on data mining. pp 499-502.
25. Robnik Šikonja M, Kononenko I (2008) Explaining classifications for individual instances. IEEE Trans Knowl Data Eng 20: 589-600.
26. Saunders C, Gammerman A, Vovk V (1999) Transduction with confidence and credibility. Proc IJCAI' 99(2): 722-726.
27. Szafron D, Poulin B, Eisner R, Lu P, Greiner R, Wishart D, Fyshe A, Pearcy B, Macdonell C, and Anvik J (2006) Visual explanation of evidence in additive classifiers. In: Proceedings of innovative applications of artificial intelligence.
28. Štrumbelj E, Kononenko I (2008) Towards a model independent method for explaining classification for individual instances. In: Proceedings of data warehousing and knowledge discovery. LNCS. Springer, Berlin, pp 273-282.
29. Towell G, Shavlik JW (1993) Extracting refined rules from knowledge-based neural networks. Mach Learn 13: 71-101.
30. Wu X, Kumar V, Quinlan JR, Ghosh J, Yang Q, Motoda H, McLachlan GJ, Ng A, Liu R, Yu PS, Steinbach ZZM, Hand DJ, Steinberg D (2008) Top 10 algorithms in data mining. Knowl Inf Syst 14: 1-37.