Title :
How many classifiers do I need?
Author_Institution :
Comput. Sci. Dept., Eidgenossische Tech. Hochschule, Zurich, Switzerland
Abstract :
Combining multiple classifiers promises to increase performance and robustness of a classification task. Currently, the understanding which combination scheme should be used and the ability to quantify the expected benefit is inadequate. This paper attempts to quantify the performance and robustness gain for different combination schemes and for two classifier types. The results indicate that the combination of a small number of classifiers may already result in a substantial performance gain. Also, the increase in robustness can be substantial by combining an adequate number of classifiers.
Keywords :
pattern classification; probability; binary classifiers; complementary classifiers; majority vote; multiple classifiers; pattern classification; performance; probability; product-rule; redundant classifiers; robustness; sum-rule; Computer science; Computer vision; Equations; Noise robustness; Pattern recognition; Performance gain; Probability; Voting;
Conference_Titel :
Pattern Recognition, 2002. Proceedings. 16th International Conference on
Print_ISBN :
0-7695-1695-X
DOI :
10.1109/ICPR.2002.1048266