Abstract
We address one of the main open issues about the use of diversity in multiple classifier systems: the effectiveness of the explicit use of diversity measures for creation of classifier ensembles. So far, diversity measures have been mostly used for ensemble pruning, namely, for selecting a subset of classifiers out of an original, larger ensemble. Here we focus on pruning techniques based on forward/backward selection, since they allow a direct comparison with the simple estimation of accuracy of classifier ensemble. We empirically carry out this comparison for several diversity measures and benchmark data sets, using bagging as the ensemble construction technique, and majority voting as the fusion rule. Our results provide further and more direct evidence to previous observations against the effectiveness of the use of diversity measures for ensemble pruning, but also show that, combined with ensemble accuracy estimated on a validation set, diversity can have a regularization effect when the validation set size is small.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
If no predefined size is given, FS stops when all the classifiers from E have been added, and returns the best ensemble among the N ones obtained at every iteration.
- 2.
- 3.
- 4.
References
Banfield, R.E., Hall, L.O., Bowyer, K.W., Kegelmeyer, W.P.: EnsembleUWA diversity measures and their application to thinning. Inf. Fusion 6(1), 49–62 (2005)
Brown, G., Wyatt, J.L., Harris, R., Yao, X.: Diversity creation methods: a survey and categorisation. Inf. Fusion 6(1), 5–20 (2005)
Brown, G., Kuncheva, L.I.: “Good” and “Bad” diversity in majority vote ensembles. In: El Gayar, N., Kittler, J., Roli, F. (eds.) MCS 2010. LNCS, vol. 5997, pp. 124–133. Springer, Heidelberg (2010)
Caruana, R., Niculescu-Mizil, A., Crew, G., Ksikes, A.: Ensemble selection from libraries of models. In: 21st International Conference on Machine Learning, p. 18. ACM (2004)
Demsar, J.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7, 1–30 (2005)
Didaci, L., Fumera, G., Roli, F.: Diversity in classifier ensembles: fertile concept or dead end? In: Zhou, Z.-H., Roli, F., Kittler, J. (eds.) MCS 2013. LNCS, vol. 7872, pp. 37–48. Springer, Heidelberg (2013)
Ko, A.H.-R., Sabourin, R., de Souza Britto Jr., A.: Compound diversity functions for ensemble selection. Int. J. Patt. Rec. Artif. Int. 23(4), 659–686 (2009)
Krogh, A., Vedelsby, J.: Neural network ensembles, cross validation, and active learning. In: Tesauro, G., Touretzky, D.S., Leen, T.K. (eds.) Advances in Neural Information Processing Systems 7, pp. 231–238. MIT Press, Cambridge (1995)
Kuncheva, L.I., Whitaker, C.J.: Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy. Mach. Learn. 51(2), 181–207 (2003)
Kuncheva, L.I.: A bound on kappa-error diagrams for analysis of classifier ensembles. IEEE Trans. Knowl. Data Eng. 25(3), 494–501 (2013)
Kuncheva, L.I.: Combining Pattern Classifiers: Methods and Algorithms, 2nd edn. Wiley, Hoboken (2014)
Li, N., Yu, Y., Zhou, Z.-H.: Diversity regularized ensemble pruning. In: Flach, P.A., De Bie, T., Cristianini, N. (eds.) ECML PKDD 2012, Part I. LNCS, vol. 7523, pp. 330–345. Springer, Heidelberg (2012)
Margineantu, D.D., Dietterich, T.G.: Pruning adaptive boosting. In: 14th International Conference Machine Learning, pp. 378–387. Morgan Kaufmann (1997)
Martinez-Munoz, G., Suarez, A.: Aggregation ordering in bagging. In: International Conference on Artificial Intelligence and Applications, pp. 258–263 (2004)
Partalas, I., Tsoumakas, G., Vlahavas, I.P.: An ensemble uncertainty aware measure for directed hill climbing ensemble pruning. Mach. Learn. 81, 257–282 (2010)
Partridge, D., Yates, W.B.: Engineering multiversion neural-net systems. Neural Comput. 8(4), 869–893 (1996)
Prodromidis, A., Stolfo, S.J.: Pruning meta-classifiers in a distributed data mining system. In: Proceedings of the 1st National Conference on New Information Technologies, pp. 151–160 (1998)
Rokach, L.: Collective-agreement-based pruning of ensembles. Comp. Stat. Data Anal. 53(4), 1015–1026 (2009)
Tang, E.K., Suganthan, P.N., Yao, X.: An analysis of diversity measures. Mach. Learn. 65, 247–271 (2006)
Tsoumakas, G., Partalas, I., Vlahavas, I.: An ensemble pruning primer. In: Okun, Oleg, Valentini, Giorgio (eds.) Applications of Supervised and Unsupervised Ensemble Methods. SCI, vol. 245, pp. 1–13. Springer, Heidelberg (2009)
Ueda, N., Nakano, R.: Generalization error of ensemble estimators. In: International Conference on Neural Networks, pp. 90–95 (1996)
Yu, Y., Li, Y.-F., Zhou, Z.-H.: Diversity regularized machine. In: 22nd International Joint Conference on Artificial Intelligence, pp. 1603–1608 (2011)
Zhou, Z.-H., Wu, J., Tang, W.: Ensembling neural networks: many could be better than all. Artif. Intell. 137(1–2), 239–263 (2002)
Yu, Y., Li, Y.-F., Zhou, Z.-H.: Diversity regularized machine. In: Proceedings of the 22nd International Joint Conference on Artificial Intelligence, pp. 1603–1608 (2011)
Zhou, Z.-H.: Ensemble Methods: Foundations and Algorithms. CRC Press, USA (2012)
Acknowledgments
This work has been partly supported by the project CRP-59872 funded by Regione Autonoma della Sardegna, L.R. 7/2007, Bando 2012.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Ahmed, M.A.O., Didaci, L., Fumera, G., Roli, F. (2015). An Empirical Investigation on the Use of Diversity for Creation of Classifier Ensembles. In: Schwenker, F., Roli, F., Kittler, J. (eds) Multiple Classifier Systems. MCS 2015. Lecture Notes in Computer Science(), vol 9132. Springer, Cham. https://doi.org/10.1007/978-3-319-20248-8_18
Download citation
DOI: https://doi.org/10.1007/978-3-319-20248-8_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-20247-1
Online ISBN: 978-3-319-20248-8
eBook Packages: Computer ScienceComputer Science (R0)