The effect of target vector selection on the invariance of classifier performance measures.

The effect of target vector selection on the invariance of classifier performance measures.
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目标向量选择对分类器性能测量不变性的影响。

DOI:
10.1109/tnn.2008.2011809
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发表时间:
2009
影响因子:
--
通讯作者:
Cartinhour,SamuelW
Cartinhour,SamuelW
中科院分区:
--
文献类型:
--
作者:
Sakk,Eric;Schneider,DavidJ;Myers,ChristopherR;Cartinhour,SamuelW

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In this paper, the multiclass supervised training problem is considered when a discrete set of classes is assumed. Upon generating affine models for finite data sets, we have observed the invariance of certain measures of performance after a trained classifier has been presented with test data of unknown classification. Specifically, after constructing mappings between training vectors and their desired targets, the class membership and ranking of test data has been found to remain either invariant or close to invariant under a transformation of the set of target vectors. Therefore, we derive conditions explaining how this type of invariance can arise when the multiclass problem is phrased in the context of linear networks. A bioinformatics example is then presented in order to demonstrate various principles outlined in this work.
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