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Undesirable effects of output normalization in multiple classifier systems
Date
2003-06-01
Author
Altincay, H
Demirekler, Mübeccel
Metadata
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This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
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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