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Strategies for classification when classes arise from a continuum

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Kjetil Aune

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kjetil.aune@nofima.no

Quality Engineering ; Volume 15. p. 113–126. 2002

Mevik, Bjørn-Helge; Næs, Tormod

The situation where classes arise from a continuum is studied. In this situation, both regression and classification can perform the class allocation. It is not obvious how to compare classifiers and regressions, and different performance measures are described and briefly discussed. Several strategies for class allocation in the present situation are discussed and evaluated. Modifications to existing methods are proposed to make them more suitable for the problem. The performance measures and a selection of class allocation methods are tested and compared in simulations, with both low-dimensional data and 100-dimensional spectroscopy-like data. They are also tested on a real spectroscopic data set. The results show that classification by means of an appropriate regression outperforms the classifiers in most situations.

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