КФУ. Карточка публикации. Design of the Best Linear Classifier for Box-Constrained Data Sets.

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DESIGN OF THE BEST LINEAR CLASSIFIER FOR BOX-CONSTRAINED DATA SETS.

Форма представления

Статьи в зарубежных журналах и сборниках

Год публикации

2022

Язык

английский

Авторы, сотрудники КФУ

Габидуллина Зульфия Равилевна, автор

Библиографическое описание на языке оригинала

Gabidullina, Z.R. Design of the Best Linear Classifier for Box-Constrained Data Sets. Lecture Notes in Computational Science and Engineering Том: 141 Страницы: 109 - 124

Аннотация

We construct a binary linear classifier for n-dimensional data sets with the special box-constrained structure. Data sets with this structure arise naturally in many real-world areas. We apply a linear separability criterion proposed in J. Optim. Theory Appl. (2012, https://doi.org/10.1007/s10957-012-0155-x). The Minkowski difference of the two data sets allows us to reduce a two-class classification problem to the problem in more easy to solve form. The greatest benefit of this reduction is that it allows to compute the parameters of a linear binary classifier by way of exact formulas. For this reason, a proposed framework has low computational costs. We rigorously prove that the developed linear classification model provides the possibility of constructing the data separator (or pseudo-separator) which really has the best estimate of its thickness. There are studied both regular and singular cases of separability arising in the theory and practice of linear classification of data sets.

Ключевые слова

data classification, separator, pseudo-separator, thickness of the geometric margin

Название журнала

Lecture Notes in Computational Science and Engineering

URL

https://link.springer.com/chapter/10.1007/978-3-030-87809-2_9

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https://repository.kpfu.ru/?p_id=271607

Полная запись метаданных

Поле DC	Значение	Язык
dc.contributor.author	Габидуллина Зульфия Равилевна	ru_RU
dc.date.accessioned	2022-01-01T00:00:00Z	ru_RU
dc.date.available	2022-01-01T00:00:00Z	ru_RU
dc.date.issued	2022	ru_RU
dc.identifier.citation	Gabidullina, Z.R. Design of the Best Linear Classifier for Box-Constrained Data Sets. Lecture Notes in Computational Science and Engineering Том: 141 Страницы: 109 - 124	ru_RU
dc.identifier.uri	https://repository.kpfu.ru/?p_id=271607	ru_RU
dc.description.abstract	Lecture Notes in Computational Science and Engineering	ru_RU
dc.description.abstract	We construct a binary linear classifier for n-dimensional data sets with the special box-constrained structure. Data sets with this structure arise naturally in many real-world areas. We apply a linear separability criterion proposed in J. Optim. Theory Appl. (2012, https://doi.org/10.1007/s10957-012-0155-x). The Minkowski difference of the two data sets allows us to reduce a two-class classification problem to the problem in more easy to solve form. The greatest benefit of this reduction is that it allows to compute the parameters of a linear binary classifier by way of exact formulas. For this reason, a proposed framework has low computational costs. We rigorously prove that the developed linear classification model provides the possibility of constructing the data separator (or pseudo-separator) which really has the best estimate of its thickness. There are studied both regular and singular cases of separability arising in the theory and practice of linear classification of data sets.	ru_RU
dc.language.iso	ru	ru_RU
dc.subject	data classification	ru_RU
dc.subject	separator	ru_RU
dc.subject	pseudo-separator	ru_RU
dc.subject	thickness of the geometric margin	ru_RU
dc.title	Design of the Best Linear Classifier for Box-Constrained Data Sets.	ru_RU
dc.type	Статьи в зарубежных журналах и сборниках	ru_RU