Formalization of ⇜natural⇝ classification and ⇜natural⇝ concepts by probabilistic generalization of formal concepts analysis

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Formalization of ⇜natural⇝ classification and ⇜natural⇝ concepts by probabilistic generalization of formal concepts analysis. / Vityaev, Evgenii E.; Degtiarev, Vladislav; Pak, Bayar и др.

в: CEUR Workshop Proceedings, Том 2648, 10.2020, стр. 59-73.

Результаты исследований: Научные публикации в периодических изданиях › статья по материалам конференции › Рецензирование

BibTeX

@article{6370a094a7ad4d63921102847b08be38,

title = "Formalization of ⇜natural⇝ classification and ⇜natural⇝ concepts by probabilistic generalization of formal concepts analysis",

abstract = "In the previous works, a probabilistic generalization of the formal concepts analysis was developed. This generalization is induced by the problem of formal concepts determining under noise conditions, when the lattice of formal concepts exponentially grows. In this paper, probabilistic formal concepts with negation are determined, as well as a statistical method for detecting these probabilistic formal concepts. The purpose of this paper is to show that probabilistic formal concepts have a deeper meaning. It is argued that probabilistic formal concepts formalize the “natural” concepts described in cognitive sciences by “causal models”, which are characterized by a highly correlated structure of attributes. The same structure is specific for the “natural{"} classification of objects of the external world. The definition of “natural{"} classification given by J. Stuart Mill is fairly accurately formalized by probabilistic formal concepts.",

author = "Vityaev, {Evgenii E.} and Vladislav Degtiarev and Bayar Pak and Yuri Meister",

note = "Publisher Copyright: {\textcopyright} 2020 Copyright for this paper by its authors. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; 2020 {"}Russian Advances in Artificial Intelligence{"}, RAAI 2020 ; Conference date: 10-10-2020 Through 16-10-2020",

year = "2020",

month = oct,

language = "English",

volume = "2648",

pages = "59--73",

journal = "CEUR Workshop Proceedings",

issn = "1613-0073",

publisher = "CEUR-WS",

}

RIS

TY - JOUR

T1 - Formalization of ⇜natural⇝ classification and ⇜natural⇝ concepts by probabilistic generalization of formal concepts analysis

AU - Vityaev, Evgenii E.

AU - Degtiarev, Vladislav

AU - Pak, Bayar

AU - Meister, Yuri

PY - 2020/10

Y1 - 2020/10

N2 - In the previous works, a probabilistic generalization of the formal concepts analysis was developed. This generalization is induced by the problem of formal concepts determining under noise conditions, when the lattice of formal concepts exponentially grows. In this paper, probabilistic formal concepts with negation are determined, as well as a statistical method for detecting these probabilistic formal concepts. The purpose of this paper is to show that probabilistic formal concepts have a deeper meaning. It is argued that probabilistic formal concepts formalize the “natural” concepts described in cognitive sciences by “causal models”, which are characterized by a highly correlated structure of attributes. The same structure is specific for the “natural" classification of objects of the external world. The definition of “natural" classification given by J. Stuart Mill is fairly accurately formalized by probabilistic formal concepts.

AB - In the previous works, a probabilistic generalization of the formal concepts analysis was developed. This generalization is induced by the problem of formal concepts determining under noise conditions, when the lattice of formal concepts exponentially grows. In this paper, probabilistic formal concepts with negation are determined, as well as a statistical method for detecting these probabilistic formal concepts. The purpose of this paper is to show that probabilistic formal concepts have a deeper meaning. It is argued that probabilistic formal concepts formalize the “natural” concepts described in cognitive sciences by “causal models”, which are characterized by a highly correlated structure of attributes. The same structure is specific for the “natural" classification of objects of the external world. The definition of “natural" classification given by J. Stuart Mill is fairly accurately formalized by probabilistic formal concepts.

UR - http://www.scopus.com/inward/record.url?scp=85092326279&partnerID=8YFLogxK

M3 - Conference article

AN - SCOPUS:85092326279

VL - 2648

SP - 59

EP - 73

JO - CEUR Workshop Proceedings

JF - CEUR Workshop Proceedings

SN - 1613-0073

T2 - 2020 "Russian Advances in Artificial Intelligence", RAAI 2020

Y2 - 10 October 2020 through 16 October 2020

ER -

ID: 25686875