Undesirable effects of output normalization in multiple classifier systems

Date

2003-06-01

Author

Altincay, H
Demirekler, Mübeccel

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Incomparability of the classifier output scores is a major problem in the combination of different classification systems. In order to deal with this problem, the measurement level classifier outputs are generally normalized. However, empirical results have shown that output normalization may lead to some undesirable effects. This paper presents analyses for some most frequently used normalization methods and it is shown that the main reason for these undesirable effects of output normalization is the dimensionality reduction in the output space. An artificial classifier combination example and a real-data experiment are provided where these effects are further clarified.

Subject Keywords

Signal Processing, Software, Artificial Intelligence, Computer Vision and Pattern Recognition

URI

https://hdl.handle.net/11511/57537

Journal

PATTERN RECOGNITION LETTERS

DOI

https://doi.org/10.1016/s0167-8655(02)00286-6

Collections

Graduate School of Natural and Applied Sciences, Article

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Citation Formats

H. Altincay and M. Demirekler, “Undesirable effects of output normalization in multiple classifier systems,” PATTERN RECOGNITION LETTERS, pp. 1163–1170, 2003, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/57537.