A study on the performances of dynamic classifier selection based on local accuracy estimation. (English) Zbl 1077.68797
Summary: Dynamic Classifier Selection (DCS) plays a strategic role in the field of multiple classifier systems. This paper proposes a study on the performances of DCS by local accuracy estimation. To this end, upper bounds against which the performances can be evaluated are proposed. The experimental results on five datasets clearly show the effectiveness of the selection methods based on local accuracy estimates.
MSC:
| 68T10 | Pattern recognition, speech recognition |
References:
| [1] | Woods, K.; Kegelmeyer, W. P.; Bowyer, K., Combination of multiple classifiers using local accuracy estimates, IEEE Trans. Pattern Anal. Mach. Intell., 19, 4, 405-410 (1997) |
| [2] | Giacinto, G.; Roli, F., Adaptive selection of image classifiers, (Proceedings of ICIAP ’97, Lecture Notes in Computer Science, vol. 1310 (1997)), 38-45 |
| [3] | Giacinto, G.; Roli, F., Methods for dynamic classifier selection, (Proceedings of ICIAP ’99. Proceedings of ICIAP ’99, Italy (1999)), 659-664 |
| [4] | Kuncheva, L. I., Switching between selection and fusion in combining classifiersan experiment, IEEE Trans. Syst. Man Cybernet. Part B, 32, 2, 146-156 (2002) |
| [5] | Hastie, T.; Tibshirani, R., Discriminant adaptive nearest neighbour classification, IEEE Trans. Pattern Anal. Mach. Intell., 18, 6, 607-615 (1996) |
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