Combining both ensemble and dynamic classifier selection schemes for prediction of mobile internet subscribers

Expert Systems with Applications

Volumn 25, Issue 1, 2003, Pages 63-68

  Shin, H W ^a   Sohn, S Y ^a  

^a Yonsei University   (South Korea)

Author keywords

Accuracy; Diversity; Dynamic classifier selection; Ensemble; Mobile internet service

Indexed keywords

CELLULAR RADIO SYSTEMS; MARKETING; MOBILE TELECOMMUNICATION SYSTEMS; QUALITY OF SERVICE;

DYNAMIC CLASSIFIER SELECTION (DCS);

INTERNET;

EID: 0037411407     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0957-4174(03)00006-X     Document Type: Article

References (16)

1. Breiman, L. (1994). Bagging predictors. Technical Report No.421, University of California at Berkeley.
2. Breiman L. Bagging predictors. Machine Learning. 24:(2):1996;123-140.
3. Christodoulou, C.I., & Pattichis, C.S. (1998). Combining neural classifications in EMG diagnosis. Proceedings of the conference on EUFIT, pp. 1837-1841.
4. Dietterich T.G. An experimental comparison of three Methods for constructing ensembles of decision trees: bagging, boosting, and randomization. Machine Learning. 40:(2):2000;139-158.
5. Giacinto G., Roli F. Dynamic classifier selection based on multiple classifier behaviour. Pattern Recognition. 34:2001;1879-1881.
6. Giacinto G., Roli F. An approach to the automatic design of multiple classifier systems. Pattern Recognition Letters. 22:2001;25-33.
7. Ho T.K. The random subspace Method for constructing decision forests. IEEE Transaction on Pattern analysis and Machine Intelligence. 21:(11):1998;832-844.
8. Kittler J., Hatef M., Duin R.P.W., Mtas J. On combining classifiers. IEEE Transaction on Pattern Analysis and Machine Intelligence. 20:(3):1998;226-239.
9. Kuncheva, L.I. (2000). Cluster and selection method for classifier combination. Proceedings of the 14th International Conference on Knowledge Based Intelligent Engineering Systems, pp. 185-188. Available at http://www.bangor.ac.uk/̃mas00a.
10. Kuncheva L.I., Whitaker C.J. Ten measures of diversity in classifier ensembles: Limits for two classifiers. Proceedings of the IEE Workshop on Intelligent Sensor Processing, Birmingham. 2001;. pp. 10/1-10/6.
11. Milligan G.W., Cooper M.C. An examination of procedures for determining the number of clusters in a data set. Psychometrika. 50:(2):1985;159-179.
12. Mojena R. Hierarchical grouping methods and stopping rules: an evaluation. The Computer Journal. 20:1977;359-363.
13. Neter, J., Kutner, M.H., Nachtsheim, C.J., & Wasserman, W. (1996). Applied linear regression models. Chicago: IRWIN.
14. Sebert D.M., Montgomery D.C., Rollier D.A. A clustering algorithm for identifying multiple outliers in linear regression. Computational Statistics and Data Analysis. 27:1998;461-484.
15. Skurichina (2000). Bagging, boosting and the random subspace method for linear classifiers. Proceedings of the conference on Pattern Analysis and Applications, pp. 121-135.
16. Woods K., Kegelmeyer W.P., Bowyer K. Combination of multiple classifiers using local accuracy estimates. IEEE Transactions on Pattern Analysis and Machine Intelligence. 19:(4):1997;405-410.