Optimal selection of ensemble classifiers using measures of competence and diversity of base classifiers

Neurocomputing

Volumn 126, Issue , 2014, Pages 29-35

  Lysiak, Rafal ^a   Kurzynski, Marek ^a   Woloszynski, Tomasz ^a  

^a WROCLAW UNIVERSITY OF TECHNOLOGY   (Poland)

Author keywords

Classifier competence; Diversity measure; Dynamic ensemble selection; Simulated annealing

Indexed keywords

CLASSIFICATION PERFORMANCE; DIVERSITY MEASURE; DYNAMIC ENSEMBLE SELECTIONS; MULTIPLE CLASSIFIER SYSTEMS; OPTIMIZATION PROBLEMS; PROBABILISTIC MODELING; SIMULATED ANNEALING ALGORITHMS; UCI MACHINE LEARNING REPOSITORY;

COMPUTER APPLICATIONS; NEURAL NETWORKS;

SIMULATED ANNEALING;

ALGORITHM; ARTICLE; CLASSIFIER; CLASSIFIER COMPETENCE; CLASSIFIER DIVERSITY; DATA BASE; DYNAMIC ENSEMBLE SELECTION SYSTEM; MACHINE LEARNING; MATHEMATICAL ANALYSIS; MATHEMATICAL MODEL; MATHEMATICAL PARAMETERS; MULTIPLE CLASSIFIER SYSTEM; PRIORITY JOURNAL; PROBABILITY; PROCESS DEVELOPMENT; PROCESS OPTIMIZATION;

EID: 84887611642     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2013.01.052     Document Type: Article

References (24)

1. Kittler J., Hatef M., Duin R., Matas J. On combining classifiers. IEEE Transactions on Pattern Analysis and Machine Intelligence 2006, 20:226-239.
2. Kuncheva I. Combining Pattern Classifiers. Methods and Algorithms 2004, Wiley-Interscience, West Florida.
3. Woods K., Kegelmeyer W., Bowyer W. Combination of multiple classifiers using local accuracy estimates. IEEE Transactions on Pattern Analysis and Machine Intelligence 1997, 19:405-410.
4. Didaci L., Giacinto G., Roli F., Marcialis G. A study on the performances of dynamic classifier selection based on local accuracy estimation. Pattern Recognition 2005, 38:2188-2191.
5. Smits P. Multiple classifier systems for supervised remote sensing image classification based on dynamic classifier selection. IEEE Transactions on Geoscience and Remote Sensing 2002, 40:717-725.
6. Huenupan F., Yoma N. Confidence based multiple classifier fusion in speaker verification. Pattern Recognition Letters 2008, 29:957-966.
7. Giacinto G., Roli F. Dynamic classifier selection based on multiple classifier behaviour. Pattern Recognition 2001, 34:1879-1881.
8. T. Woloszynski, M. Kurzynski, A probabilistic model of classifier competence for dynamic ensemble selection, Pattern Recognition 44 (2011) 2656-2668.
9. Aksela M., Laaksonen J. Using diversity of errors for selecting members of a committee classifier. Pattern Recognition 2006, 39:608-623.
10. Kuncheva L., Whitaker C. Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy. Machine Learning 2003, 51:181-207.
11. E. Corchado, A. Abraham, A. de Carvalho, Editorial: hybrid intelligent algorithms and applications, Information Sciences: an International Journal 180 (2010) 2633-2634.
12. Kirkpatrick S., Gelatt C.D., Vecchi M.P. Optimization by simulated annealing. Science 1983, 220:621-630.
13. M. Aksela, Comparison of classifier selection methods for improving committee performance, in: Proceedings of the Multiple Classifier Systems, pp. 84-93.
14. Berger J. Statistical Decision Theory and Bayesian Analysis 1987, Springer Verlag, New York.
15. R. Lysiak, M. Kurzynski, T. Woloszynski, Probabilistic approach to the dynamic ensemble selection using measures of competence and diversity of base classifiers, in: Proceedings of the Hybrid Artificial Intelligent Systems. Part II. Lecture Notes in Computer Science, vol. 6678, 2011, pp. 345-351.
16. T. Woloszynski, Matlab Central File Enchange, 2010. http://www.mathwork.com/~matlabcentral/~fileenchange/~28391-classifier-competence-based-on-probabilistic-modeling.
17. Liu J. Algorithm of QOS multicast routing based on genetic simulated annealing algorithm. Computer Application and System Modeling (ICCASM) 2010, 5. pp. V5-220-V5-223.
18. Aly A., Hegazy Y., Alsharkawy M. A simulated annealing algorithm for multi-objective distributed generation planning. Power and Energy Society General Meeting 2010, 1-7.
19. Queirolo C., Silva L., Bellon O., Segundo M. 3d face recognition using simulated annealing and the surface interpenetration measure. Pattern Analysis and Machine Intelligence 2010, 32:206-219.
20. L. Zhong, J. Sheng, M. Jing, Z. Yu, X. Zeng, D. Zhou, An optimized mapping algorithm based on simulated annealing for regular NOC architecture, in: 9th International Conference on ASIC, 2011, pp. 389-392.
21. Bertsimas D., Tsitsiklis J. Simulated annealing. Statistical Science 1993, 8:10-15.
22. R. Duin, P. Juszczak, P. Paclik, PRTools4. A Matlab Toolbox for Pattern Recognition, Delft University of Technology, Delft, 2007.
23. Duda R., Hart P., Stork G. Pattern Classification 2000, John Wiley and Sons, New York.
24. Dietterich T. Approximate statistical tests for comparing supervised classification learning algorithms. Neural Computation 1998, 10:1895-1923.