Enhancing data stream predictions with reliability estimators and explanation

Engineering Applications of Artificial Intelligence

Volumn 34, Issue , 2014, Pages 178-192

  Bosnić, Zoran ^a   Demšar, Jaka ^a   Kešpret, Grega ^a   Pereira Rodrigues, Pedro ^b   Gama, João ^c   Kononenko, Igor ^a  

^a UNIVERSITY OF LJUBLJANA   (Slovenia)

^b UNIVERSITY OF PORTO   (Portugal)

^c UNIVERSITY OF PORTO   (Portugal)

Author keywords

Data stream; Incremental learning; Prediction accuracy; Prediction correction; Prediction explanation

Indexed keywords

DATA COMMUNICATION SYSTEMS;

DATA STREAM; DATA STREAM PREDICTIONS; INCREMENTAL LEARNING; INCREMENTAL MODELING; PREDICTION ACCURACY; PREDICTION CORRECTION; PREDICTION ERRORS; VISUALIZATION TECHNIQUE; DYNAMIC STREAMING;

FORECASTING;

EID: 84904208247     PISSN: 09521976     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.engappai.2014.06.001     Document Type: Article

References (43)

1. R. Andrews, J. Diederich, and A.B. Tickle Survey and critique of techniques for extracting rules from trained artificial neural networks Knowl.-Based Syst. 8 1995 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. A. Bifet, G. Holmes, R. Kirkby, and B. Pfahringer Moa: Massive online analysis J. Mach. Learn. Res. 11 2010 1601 1604
4. Z. Bosnić, and I. Kononenko Comparison of approaches for estimating reliability of individual regression prediction Data Knowl. Eng. 67 2008 504 516
5. Z. Bosnić, and I. Kononenko Estimation of individual prediction reliability using the local sensitivity analysis Appl. Intell. 29 2008 187 203
6. Z. Bosnić, and I. Kononenko An overview of advances in reliability estimation of individual predictions in machine learning Intell. Data Anal. 13 2009 385 401
7. Z. Bosnić, P. Vračar, M. Radović, G. Devedžić, N. Filipović, and I. Kononenko Mining data from hemodynamic simulations for generating prediction and explanation models IEEE Trans. Inf. Technol. Biomed.: A Publ. IEEE Eng. Med. Biol. Soc. 2 2012 248 254
8. O. Bousquet, and A. Elisseeff Stability and generalization J. Mach. Learn. Res. 2 2002 499 526
9. L. Breiman Random forests Mach. Learn. J. 45 2001 5 32
10. D. Bunn, and E. Farmer Comparative Models for Electrical Load Forecasting 1985 Wiley New York
11. Carney, J.; Cunningham, P.; 1999. Confidence and prediction intervals for neural network ensembles. In: Proceedings of IJCNN'99, The International Joint Conference on Neural Networks, Washington, USA, pp. 1215-1218.
12. Craven, M.W.; 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.
13. D.J. Fox An R and S Plus Companion to Applied Regression 1st edition 2002 Sage Publications, Inc
14. J. Gama Knowledge Discovery from Data Streams 1st edition 2010 Chapman & Hall/CRC chapter 3, pp. 42-47
15. J. Gama, and P. Rodrigues Learning from Data Streams - Processing Techniques in Sensor Networks 2007 Springer Verlag chapter 3, pp. 25-39
16. J. Gama, R. Sebastião, and P.P. Rodrigues On evaluating stream learning algorithms Mach. Learn. 90 2013 317 346
17. 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.
18. 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, Toronto, Canada, pp. 1-8.
19. T. Heskes Practical confidence and prediction intervals M.C. Mozer, M.I. Jordan, T. Petsche, Advances in Neural Information Processing Systems 1997 The MIT Press 176 182
20. Hulten, G.; Spencer, L.; Domingos, P.; 2001. Mining time-changing data streams. In: Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 97-106.
21. 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, ACM, New York, NY, USA. pp. 108-117.
22. I. Kononenko Inductive and bayesian learning in medical diagnosis Appl. Artif. Intell. 7 1993 317 337
23. Kuhn, M.; 2012. Variable Selection Using the Caret Package. URL http://cran.cermin.lipi.go.id/web/packages/caret/vignettes/caretSelection.pdf .
24. E. Kyriakides, and M. Polycarpou Short term electric load forecasting a tutorial Trends Neural Comput. 2007 391 418
25. Lemaire, V.; Féraud, R.; Voisine, N.; 2008. Contact personalization using a score understanding method. In: International Joint Conference on Neural Networks (IJCNN).
26. F. Li, and H. Wechsler Open set face recognition using transduction IEEE Trans. Pattern Anal. Mach. Intell. 27 2005 1686 1697
27. 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, Springer-Verlag New York, Inc.; New York, NY, USA, pp. 337-348.
28. Nouretdinov, I.; Melluish, T.; Vovk, V.; 2001. Ridge regression confidence machine. In: Proceedings of 18th International Conference on Machine Learning. Morgan Kaufmann, San Francisco, CA, pp. 385-392.
29. NYISO, 2012. New York Independent System Operator. Load Data. URL http://www.nyiso.com/public/markets-operations/market-data/load-data/index.jsp .
30. Poulet, F.; 2004. SVM and graphical algorithms: a cooperative approach. In: Proceedings of Fourth IEEE International Conference on Data Mining, pp. 499-502.
31. M. Robnik-Šikonja, and I. Kononenko Explaining classifications for individual instances IEEE Trans. Knowl. Data Eng. 20 2008 589 600
32. Rodrigues, P.; Bosnić, Z.; Gama, J.; 2008. Online reliability estimates for individual predictions in data streams. In: Bonchi, F. (Ed.), ICDM Workshops 2008: Proceedings, Pisa, Italy. pp. 36-45.
33. P. Rodrigues, and J. Gama A system for analysis and prediction of electricity load streams Intell. Data Anal. 13 2009 477 496
34. Rodrigues, P.; Gama, J.; Sebastiao, R.; 2010. Memoryless fading windows in ubiquitous settings. In: Proceedings of Ubiquitous Data Mining (UDM) Workshop in Conjunction with the 19th ECAI 2010, Lisbon, pp. 27-32.
35. A.L. de Santana, C. Frances, C.A. Rocha, S.V. Carvalho, N.L. Vijaykumar, L.P. Rego, and J.C. Costa Strategies for improving the modeling and interpretability of bayesian networks Data Knowl. Eng. 63 2007 91 107
36. Saunders, C.; Gammerman, A.; Vovk, V.; 1999. Transduction with confidence and credibility. In: Proceedings of IJCAI'99, pp. 722-726.
37. Sebastião, R.; Gama, J.; 2009. A study on change detection methods. In: Lopes, L.S.; Lau, N.; Mariano, P.; Rocha, L.M. (Eds.), Progress in Artificial Intelligence, 14th Portuguese Conference on Artificial Intelligence, EPIA 2009, Aveiro, Portugal, October 12-15, 2009. Proceedings, Springer, pp. 353-264.
38. Street, W.N.; Kim, Y.; 2001. A streaming ensemble algorithm (sea) for large-scale classification. In: Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, New York, NY, USA, pp. 377-382.
39. G. Towell, and J.W. Shavlik Extracting refined rules from knowledge-based neural networks, machine learning Mach. Learn. 13 1993 71 101
40. E. Štrumbelj, Z. Bosnić, I. Kononenko, B. Zakotnik, and C. Grašič-Kuhar Explanation and reliability of prediction models the case of breast cancer recurrence Knowl. Inf. Syst. 9 2010 305 324
41. E. Štrumbelj, and I. Kononenko An efficient explanation of individual classifications using game theory J. Mach. Learn. Res. 11 2010 1 18
42. E. Štrumbelj, I. Kononenko Robnik Šikonja Explaining instance classifications with interactions of subsets of feature values Data Knowl. Eng. 68 2009 886 904
43. Weigend, A.; Nix, D.; 1994. Predictions with confidence intervals (local error bars). In: Proceedings of the International Conference on Neural Information Processing (ICONIP'94). Seoul, Korea, pp. 847-852.