Non-uniform layered clustering for ensemble classifier generation and optimality

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6443 LNCS, Issue PART 1, 2010, Pages 551-558

  Rahman, Ashfaqur ^a   Verma, Brijesh ^a   Yao, Xin ^b  

^a CQUni ^*  (Australia)

^b UNIVERSITY OF BIRMINGHAM   (United Kingdom)

Author keywords

ensemble classifier; genetic algorithm; optimal clustering

Indexed keywords

BASE CLASSIFIERS; CLUSTERING APPROACH; DATA SETS; ENSEMBLE CLASSIFIERS; ENSEMBLE OF CLASSIFIERS; GENERATION METHOD; LAYERED CLUSTERING; MAJORITY VOTING; NONUNIFORM; OPTIMAL CLUSTERING; OPTIMAL NUMBER; OPTIMALITY; VARIABLE NUMBER; VARIABLE NUMBER OF CLUSTERS;

CLASSIFIERS; DATA PROCESSING; GENETIC ALGORITHMS; OPTIMIZATION;

CLUSTERING ALGORITHMS;

EID: 78650180662     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-17537-4_67     Document Type: Conference Paper

References (17)

1. Tang, E.K., Suganthan, P.N., Yao, X.: An analysis of diversity measures. Machine Learning 65, 247-271 (2006)
2. Kuncheva, L.I., Whitaker, C.J.: Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy. Machine Learning 51(2), 181-207 (2003)
3. Scherbart, A., Nattkemper, T.W.: The diversity of regression ensembles combining bagging and random subspace method. In: Köppen, M., Kasabov, N., Coghill, G. (eds.) ICONIP 2008. LNCS, vol. 5507, pp. 911-918. Springer, Heidelberg (2009)
4. Breiman, L.: Bagging predictors. Machine Learning 24(2), 123-140 (1996)
5. Chen, L., Kamel, M.S.: A generalized adaptive ensemble generation and aggregation approach for multiple classifiers systems. Pattern Recognition 42, 629-644 (2009)
6. Nanni, L., Lumini, A.: Fuzzy bagging: a novel ensemble of classifiers. Pattern Recognition 39, 488-490 (2006) (Pubitemid 41808629)
7. Schapire, R.E.: The strength of weak learnability. Machine Learning 5(2), 197-227 (1990)
8. Freund, Y., Schapire, R.E.: Decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences 55(1), 119-139 (1997)
9. Muhlbaier, M.D., Topalis, A., Polikar, R.: Learn++. NC: Combining ensemble of classifiers with dynamically weighted consult-and-vote for efficient incremental learning of new classes. IEEE Trans. on Neural Networks 20(1), 152-168 (2009)
10. Nascimento, D.S.C., Coelho, A.L.V.: Ensembling heterogeneous learning models with boosting. In: Leung, C.S., Lee, M., Chan, J.H. (eds.) ICONIP 2009. LNCS, vol. 5863, pp. 512-519. Springer, Heidelberg (2009)
11. Rokach, L., Maimon, O., Lavi, I.: Space decomposition in data mining: a clustering approach. In: Int. Symp. on Methodologies for Intelligent Systems, Maebashi, Japan, pp. 24-31 (2003)
12. Kuncheva, L.I.: Switching between selection and fusion in combining classifiers: an experiment. IEEE Trans on Systems, Man and Cybernetics 32(2), 146-156 (2002)
13. Nasierding, G., Tsoumakas, G., Kouzani, A.Z.: Clustering based multi-label classification for image annotation and retrieval. In: IEEE Int. Conf. on Systems, Man and Cybernetics, pp. 4514-4519 (2009)
14. Mulder, W.D., Schliebs, S., Boel, R., Kuiper, M.: Initialization dependence of clustering algorithms. In: Köppen, M., Kasabov, N., Coghill, G. (eds.) ICONIP 2008. LNCS, vol. 5507, pp. 615-622. Springer, Heidelberg (2009)
15. UCI Machine Learning Database, http://archive.ics.uci.edu/ml/
16. Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. SIGKDD Explorations 11(1) (2009)
17. LIBSVM, http://www.csie.ntu.edu.tw/~cjlin/libsvm/