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Recovering noisy contexts with probabilistic formal concepts? / Martynovich, Vitaliy V.; Vityaev, Euvgeniy E.

In: CEUR Workshop Proceedings, Vol. 1687, 2016, p. 24-35.

Research output: Contribution to journal › Conference article › peer-review

Harvard

Martynovich, VV & Vityaev, EE 2016, 'Recovering noisy contexts with probabilistic formal concepts?', CEUR Workshop Proceedings, vol. 1687, pp. 24-35.

APA

Martynovich, V. V., & Vityaev, E. E. (2016). Recovering noisy contexts with probabilistic formal concepts? CEUR Workshop Proceedings, 1687, 24-35.

Vancouver

Martynovich VV, Vityaev EE. Recovering noisy contexts with probabilistic formal concepts? CEUR Workshop Proceedings. 2016;1687:24-35.

Author

Martynovich, Vitaliy V. ; Vityaev, Euvgeniy E. / Recovering noisy contexts with probabilistic formal concepts?. In: CEUR Workshop Proceedings. 2016 ; Vol. 1687. pp. 24-35.

BibTeX

@article{59d39595a05d43ea89c33e2ddf673783,
title = "Recovering noisy contexts with probabilistic formal concepts?",
abstract = "The uncertainty in the environment typically generates noisy concept alternatives and leads to an overpopulated concept lattice. From a computational point of view, a straightforward filtering of the noisy concept lattice will suffer from an exponential-size computational overkill, and from a semantical one [ will face numerous ambiguities due to an overfitting. We managed to bypass the filtering problem by applying a sort of probabilistic approach. We developed a probabilistic generaliza-tion of formal concepts which seems to avoid a monstrous combinatorial complexity of a complete context lattice construction. The theoretical base for this method is described, as well as a ready-to-work noise resis-tant algorithm. The algorithm has been tested and showed a moderate precision and recall rate on various datasets, including a toy one pre-sented with the presence of a 2, 3 or 5% random noise.",
keywords = "Association rules, Classification task, Concept lattice, Data mining, Formal concept analysis, Inductive learning",
author = "Martynovich, {Vitaliy V.} and Vityaev, {Euvgeniy E.}",
year = "2016",
language = "English",
volume = "1687",
pages = "24--35",
journal = "CEUR Workshop Proceedings",
issn = "1613-0073",
publisher = "CEUR-WS",
note = "2nd International Workshop on Soft Computing Applications and Knowledge Discovery, SCAKD 2016 ; Conference date: 18-07-2016",

}

RIS

TY - JOUR

T1 - Recovering noisy contexts with probabilistic formal concepts?

AU - Martynovich, Vitaliy V.

AU - Vityaev, Euvgeniy E.

PY - 2016

Y1 - 2016

N2 - The uncertainty in the environment typically generates noisy concept alternatives and leads to an overpopulated concept lattice. From a computational point of view, a straightforward filtering of the noisy concept lattice will suffer from an exponential-size computational overkill, and from a semantical one [ will face numerous ambiguities due to an overfitting. We managed to bypass the filtering problem by applying a sort of probabilistic approach. We developed a probabilistic generaliza-tion of formal concepts which seems to avoid a monstrous combinatorial complexity of a complete context lattice construction. The theoretical base for this method is described, as well as a ready-to-work noise resis-tant algorithm. The algorithm has been tested and showed a moderate precision and recall rate on various datasets, including a toy one pre-sented with the presence of a 2, 3 or 5% random noise.

AB - The uncertainty in the environment typically generates noisy concept alternatives and leads to an overpopulated concept lattice. From a computational point of view, a straightforward filtering of the noisy concept lattice will suffer from an exponential-size computational overkill, and from a semantical one [ will face numerous ambiguities due to an overfitting. We managed to bypass the filtering problem by applying a sort of probabilistic approach. We developed a probabilistic generaliza-tion of formal concepts which seems to avoid a monstrous combinatorial complexity of a complete context lattice construction. The theoretical base for this method is described, as well as a ready-to-work noise resis-tant algorithm. The algorithm has been tested and showed a moderate precision and recall rate on various datasets, including a toy one pre-sented with the presence of a 2, 3 or 5% random noise.

KW - Association rules

KW - Classification task

KW - Concept lattice

KW - Data mining

KW - Formal concept analysis

KW - Inductive learning

UR - http://www.scopus.com/inward/record.url?scp=84990833594&partnerID=8YFLogxK

M3 - Conference article

AN - SCOPUS:84990833594

VL - 1687

SP - 24

EP - 35

JO - CEUR Workshop Proceedings

JF - CEUR Workshop Proceedings

SN - 1613-0073

T2 - 2nd International Workshop on Soft Computing Applications and Knowledge Discovery, SCAKD 2016

Y2 - 18 July 2016

ER -

ID: 25327581