Synonyms
Classifier combination; Ensemble learning; Multiple classifiers; Multiple expert systems
Definition
The rationale behind the growing interest in multiple classifier systems is the acknowledgment that the classical approach to design a pattern recognition system that focuses on finding the best individual classifier has some serious drawbacks. The most common type of multiple classifier system (MCS) includes an ensemble of classifiers and a function for parallel combination of classifier outputs. However, a great number of methods for creating and combining multiple classifiers have been proposed in the last 15 years. Although reported results showed the good performances achievable by combining multiple classifiers, so far a designer of pattern classification systems should regard the MCS approach as an additional tool to be used when building a single classifier with the required performance is very difficult or does not allow exploiting the complementary discriminatory...
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References
L.I. Kuncheva, Combining Pattern Classifiers: Methods and Algorithms (Wiley, New York, 2004)
T.G. Dietterich, Ensemble Methods in Machine Learning, Multiple Classifier Systems. LNCS, vol. 1857 (Springer, Berlin/New York, 2000), pp. 1–15
G. Fumera, F. Roli, A theoretical and experimental analysis of linear combiners for multiple classifier systems. IEEE Trans. Pattern Anal. Mach. Intell. 27(6), 942–956 (2005)
F. Roli, G. Giacinto, Design of multiple classifier systems, in Hybrid Methods in Pattern Recognition, ed. by H. Bunke, A. Kandel (World Scientific, River Edge, 2002)
T.K. Ho, Complexity of Classification Problems and Comparative Advantages of Combined Classifiers. LNCS, vol. 1857 (Springer, Berlin/New York, 2000), pp. 97–106
R. Jacobs, M. Jordan, S. Nowlan, G. Hinton, Adaptive mixtures of local experts. Neural Comput. 3, 79–87 (1991)
L.I. Kuncheva, C.J. Whitaker, Measures of diversity in classifier ensembles. Mach. Learn. 51, 181–207 (2003)
L. Breiman, Bagging predictors. Mach. Learn. 24, 123–140 (1996)
Y. Freund, R.E. Schapire, A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 55(1), 119–139 (1997)
T.K. Ho, The random subspace method for constructing decision forests. IEEE Trans. Pattern Anal. Mach. Intell. 20, 832–844 (1998)
T.G. Dietterich, G. Bakiri, Solving multiclass learning problems via error-correcting output codes. J. Artif. Intell. Res. 2, 263–286 (1995)
F. Roli, S. Raudys, G.L. Marcialis, An experimental comparison of fixed and trained fusion rules for crisp classifiers outputs, in Proceedings of the Third International Workshop on Multiple Classifier Systems (MCS 2002), Cagliari, June 2002. LNCS, vol. 2364, 2002, pp. 232–241
L. Xu, A. Krzyzak, C.Y. Suen, Methods for combining multiple classifiers and their applications to handwriting recognition. IEEE Trans. Syst. Man Cybern. 22(3), 418–435 (1992)
K. Woods, W.P. Kegelmeyer, K. Bowyer, Combination of multiple classifiers using local accuracy estimates. IEEE Trans. Pattern Anal. Mach. Intell. 19(4), 405–410 (1997)
G. Giacinto, F. Roli, Dynamic classifier selection based on multiple classifier behavior. Pattern Recognit. 34(9), 179–181 (2001)
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Roli, F. (2015). Multiple Classifier Systems. In: Li, S.Z., Jain, A.K. (eds) Encyclopedia of Biometrics. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7488-4_148
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DOI: https://doi.org/10.1007/978-1-4899-7488-4_148
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