The use of entropy to measure structural diversity

ICCC 2008 - IEEE 6th International Conference on Computational Cybernetics, Proceedings

Volumn , Issue , 2008, Pages 41-45

  Masisi, L ^a   Nelwamondo, V ^a   Marwala, T ^a  

^a UNIVERSITY OF THE WITWATERSRAND   (South Africa)

Author keywords

[No Author keywords available]

Indexed keywords

DIVERSITY INDICES; ENTROPY-BASED METHODS; OPTIMAL ENSEMBLES; SHANNON ENTROPIES; STRUCTURAL DIVERSITIES; UNCERTAINTY RULES;

CLASSIFIERS; CONTROL THEORY; CYBERNETICS; INFORMATION THEORY; LEARNING SYSTEMS;

ENTROPY;

EID: 61649111831     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCCYB.2008.4721376     Document Type: Conference Paper

References (19)

1. J. Kittler, M. Hatef, R. Matas and J. Duin. On combining classifiers. Intell. 20 (3). 1998. pp. 226-239.
2. L. Breiman. Combining predictors in: A.J.C Sharkey (Ed.), Combining Artificial Neural Nets. Springer. London : s.n., 1999.
3. H. Drucker. Boosting using using neural networks, in: A.J.C. Sharkey (Ed.), Combining Artificial Neural Nets. Springer. London : s.n., 1999.
4. G. mordechai, J.H. May and W.E. Spangler,. Assessing the predictive accuracy of diversity measures with domain-dependent, asymetric misclassiflcation costs. Information Fussion 6. 2005. pp. 37-48.
5. K. Sirlantzis, S. Hoque and M.C. Fairhurst. Diversity in multiple classifier ensembles based on binary feature quantisation with application to face recognition. Department of Electronics, University of Kent. United Kindom : s.n., 2008. pp. 437-445.
6. F. Giacinto and G. Roli. Hybrid Methods in Pattern Recognition. World Scientific Press. Singapore : s.n., 2002.
7. K.M. Ali and M.J. Pazzani. Error reduction through learning multiple descriptions. Machine Learning 24 (3). 1996. pp. 173-202.
8. A. Krogh and J. Vedelsby. Neural network ensembles cross validation and active learning, in: G. Tesauro, D.S. Touretzky, T.K. Leen (Eds.). MIT Press, Cambridge. 1995.
9. L. Masisi, V. Nelwamondo and T. Marwala. The effect of structural diversity of an ensemble of classifiers on classification accuracy. Johannesburg : Witwatersrand University, 2008.
10. Schneider, T.D. Information Theory Primer. s.l. : www.lecb.ncifcrf.gov/~toms, 2007.
11. I. Harrison, M. Laverty and E. Sterling. Species Diversity, s.1. : Connexions Project, 2004.
12. L.I. Kuncheva, M. Duin and R.P.W. Skurichina. An experiment study on diversity for bagging and boosting with linear classifiers. Information Fusion 3. 2002. pp. 248-250.
13. K. Turner and J. Ghosh. Linear and order statistics combiners for pattern classification. Combining Artificial Neural Nets. London : s.n., 1999.
14. A.L. Nsakanda, W.L. Price, M. Diaby and M. Gravel. Ensuring population diversity in genetic algorithm: A technical note with application to cell formation problem, s.1. : Elsevier, 2006. pp. 634-638.
15. R. Kindt, A. Degrande, L. Turyomurugyendo, C. Mbosso, P. Van Damme and A.J. Simons. Comparing species richness and evenness contributions to on farm tree diversity for data sets with varying sample sizes from Kenya, Uganda, Cameroon and Nigeria with randomized diversity profiles. Ghent University. Belgium : s.n.
16. Baumgartner, S. Why the measurement of species diversity requires prior value judgements. Department of economics, University of Heidelberg . Germany : s.n., 2006.
17. C.M. Bishop. Neural Networks for Pattern Recognition, s.1. : Oxford University Press, 1995.
18. Mohamed, S. Dynamic Protein Classification: Adaptive Models Based on Incremental Learning Strategies. School of Electrical and Information Engineering, The University of the Witwatersrand, Theses. 2006.
19. Lagazio, M. and Marwala, T. Modeling and Controlling Interstate Conflict. Witwatersrand . Johannesburg : The University Of the Witwatersrand.