Explaining instance classifications with interactions of subsets of feature values

Data and Knowledge Engineering

Volumn 68, Issue 10, 2009, Pages 886-904

  Strumbelj E ^a   Kononenko, I ^a   Robnik Sikonja M ^a  

^a UNIVERSITY OF LJUBLJANA   (Slovenia)

Author keywords

Classification; Data mining; Explanation; Knowledge discovery; Machine learning; Visualization

Indexed keywords

ARTIFICIAL DATA; BREAST CANCER; CLASSIFICATION; DATA SETS; EXPLANATION; KNOWLEDGE DISCOVERY; MACHINE LEARNING; NOVEL METHODS; PREDICTION PROBLEM; PREDICTION QUALITY; REAL-WORLD;

CLASSIFIERS; DATA VISUALIZATION; DECISION MAKING; MINING; ROBOT LEARNING; VISUALIZATION;

CLASSIFICATION (OF INFORMATION);

EID: 69249209926     PISSN: 0169023X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.datak.2009.01.004     Document Type: Article

References (25)

1. Akadi A.E., Ouardighi A.E., and Driss A. A powerful feature selection approach based on mutual information. International Journal of Computer Science and Network Security 8 (2008) 116-121
2. A. Asuncion, D. Newman, UCI machine learning repository. , 2008.
3. A.J. Bell, The co-information lattice, in: Proceedings of the International Workshop on Independent Component Analysis and Blind Signal Separation, Nara, Japan, 2003, pp. 921-926.
4. Breiman L. Random forests. Machine Learning Journal 45 (2001) 5-32
5. Brier G.W. Verification of forecasts expressed in terms of probability. Monthly Weather Review 75 (1950) 1-3
6. Cano J.R., Herrera F., and Lozano M. Evolutionary stratified training set selection for extracting classification rules with trade off precision-interpretability. Data and Knowledge Engineering 60 1 (2007) 90-108
7. de Santana A.L., Frances C., Rocha C.A., Carvalho S.V., Vijaykumar N.L., Rego L.P., and Costa J.C. Strategies for improving the modeling and interpretability of Bayesian networks. Data and Knowledge Engineering 63 1 (2007) 91-107
8. Guyon I., and Elisseeff A. An introduction to variable and feature selection. Journal of Machine Learning Research 3 (2003) 1157-1182
9. Han T.S. Multiple mutual informations and multiple interactions in frequency data. Information and Control 46 1 (1980) 26-45
10. A. Jakulin, Machine learning based on attribute interactions, Ph.D. Thesis, University of Ljubljana, Faculty of Computer and Information Science, Ljubljana. , 2005.
11. Jakulin A., Možina M., Demšar J., Bratko I., and Zupan B. Nomograms for visualizing support vector machines. KDD'05: Proceeding of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery in Data Mining (2005), ACM, New York, NY, USA 108-117
12. Kohavi R., and John G. Wrappers for feature subset selection. Artificial Intelligence journal 97 1-2 (1997) 273-324
13. Kononenko I. Machine learning for medical diagnosis: history, state of the art and perspective. Artificial Intelligence in Medicine 23 (2001) 89-109
14. Kononenko I., and Kukar M. Machine Learning and Data Mining: Introduction to Principles and Algorithms (2007), Horwood Publ.
15. Y. Lei, L. Huan, Feature selection for high-dimensional data: a fast correlation-based filter solution, in: Proceedings of the Twentieth International Conference on Machine Leaning (ICML-03), Washington, DC, 2003, pp. 856-863.
16. V. Lemaire, R. Fraud, N. Voisine, Contact personalization using a score understanding method, in: International Joint Conference on Neural Networks (IJCNN), 2008.
17. Liu H., and Yu L. Toward integrating feature selection algorithms for classification and clustering. Knowledge and Data Engineering 17 4 (2005) 491-502
18. Lubsen J., Pool J., and 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
19. Možina M., Demšar J., Kattan M., and Zupan B. Nomograms for visualization of naive bayesian classifier. PKDD '04: Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases (2004), Springer-Verlag New York, Inc., New York, NY, USA 337-348
20. Robnik-Šikonja M., and Kononenko I. Explaining classifications for individual instances. IEEE Transactions on Knowledge and Data Engineering 20 (2008) 589-600
21. Stanley R.P. Enumerative Combinatorics vol. 1-2 (1999), Cambridge University Press
22. D. Szafron, B. Poulin, R. Eisner, P. Lu, R. Greiner, D. Wishart, A. Fyshe, B. Pearcy, C. Macdonell, J. Anvik, Visual explanation of evidence in additive classifiers, in: Proceedings of Innovative Applications of Artificial Intelligence, 2006.
23. S. Thrun, J. Bala, E. Bloedorn, I. Bratko, B. Cestnik, J. Cheng, K.D. Jong, S. Dzeroski, R. Hamann, K. Kaufman, S. Keller, I. Kononenko, J. Kreuziger, R. Michalski, T. Mitchell, P. Pachowicz, B. Roger, H. Vafaie, W.V. de Velde, W. Wenzel, J. Wnek, J. Zhang, The MONK's problems: a performance comparison of different learning algorithms, Tech. Rep. CMU-CS-91-197, Carnegie Mellon University, Computer Science Department, Pittsburgh, PA, 1991.
24. Towell G., and Shavlik J.W. Extracting refined rules from knowledge-based neural networks, machine learning. Machine Learning 13 (1993) 71-101
25. Yeung R.W. A new outlook of Shannon's information measures. IEEE Transactions on Information Theory 37 3 (1991) 466-474