Post-processing of classifier outputs in multiple classifier systems

Date

2002-01-01

Author

Altincay, H
Demirekler, Mübeccel

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Incomparability in classifier outputs due to the variability in their scales is a major problem in the combination of different classification systems. In order to compensate this, output normalization is generally performed where the main aim is to transform the outputs onto the same scale. In this paper, it is proposed that in selecting the transformation function, the scale similarity goal should be accomplished with two more requirements. The first one is the separability of the pattern classes in the transformed output space and the second is the compatibility of the outputs with the combination rule. A method of transformation that provides improved satisfaction of the additional requirements is proposed which is shown to improve the classification performance of both linear and Bayesian combination systems based on the use of confusion matrix based a posteriori probabilities....

Subject Keywords

Speaker identification, Combination, Recognition

URI

https://hdl.handle.net/11511/52954

Journal

MULTIPLE CLASSIFIER SYSTEMS

Collections

Graduate School of Natural and Applied Sciences, Article

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Citation Formats

H. Altincay and M. Demirekler, “Post-processing of classifier outputs in multiple classifier systems,” MULTIPLE CLASSIFIER SYSTEMS, pp. 159–168, 2002, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/52954.