Geodesic
Regression models in the classification problem
Sibirskij žurnal industrialʹnoj matematiki, Tome 17 (2014) no. 1, pp. 86-98

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We carry out a comparative analysis of the efficiency of several methods of classification (pattern recognition) based on regression models, in particular, logistic regression and its modifications. A new method for constructing a decision function is proposed. The method is based on the maximization of the area under the error curve over the linear functions in the space of the decision functions obtained by a special transformation. The performance of the method is illustrated by solving an applied problem.
Keywords: regression analysis, pattern recognition, machine learning, decision function, misclassification probability, logistic regression, Fisher linear discriminant. 
@article{SJIM_2014_17_1_a9,
     author = {V. M. Nedel'ko},
     title = {Regression models in the classification problem},
     journal = {Sibirskij \v{z}urnal industrialʹnoj matematiki},
     pages = {86--98},
     publisher = {mathdoc},
     volume = {17},
     number = {1},
     year = {2014},
     language = {ru},
     url = {http://geodesic.mathdoc.fr/item/SJIM_2014_17_1_a9/}
}
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V. M. Nedel'ko. Regression models in the classification problem. Sibirskij žurnal industrialʹnoj matematiki, Tome 17 (2014) no. 1, pp. 86-98. http://geodesic.mathdoc.fr/item/SJIM_2014_17_1_a9/