期刊
IEEE TRANSACTIONS ON CYBERNETICS
卷 48, 期 2, 页码 703-715出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2017.2653223
关键词
Complexity of classification; extreme learning machine; generalization; uncertainty
类别
资金
- National Natural Science Foundation of China [61402460, 61472257, 61170040, 71371063]
- Basic Research Project of Knowledge Innovation Program in Shenzhen [JCYJ20150324140036825]
- Guangdong Provincial Science and Technology Plan Project [2013B040403005]
- HD Video Research and Development Platform for Intelligent Analysis and Processing in Guangdong Engineering Technology Research Centre of Colleges and Universities [GCZX-A1409]
The generalization ability of a classifier learned from a training set is usually dependent on the classifier's uncertainty, which is often described by the fuzziness of the classifier's outputs on the training set. Since the exact dependency relation between generalization and uncertainty of a classifier is quite complicated, it is difficult to clearly or explicitly express this relation in general. This paper shows a specific study on this relation from the viewpoint of complexity of classification by choosing extreme learning machines as the classification algorithms. It concludes that the generalization ability of a classifier is statistically becoming better with the increase of uncertainty when the complexity of the classification problem is relatively high, and the generalization ability is statistically becoming worse with the increase of uncertainty when the complexity is relatively low. This paper tries to provide some useful guidelines for improving the generalization ability of classifiers by adjusting uncertainty based on the problem complexity.
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