Rough sets

Pawlak, Zdzisław

doi:10.1007/bf01001956

Cited by 11,740 publications

(4,803 citation statements)

References 2 publications

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“…Let, for example, the values of variables be given in terms of fuzzy mathematics [29]. A possible approach to solving the problem in this case is the formation of fuzzy models of inaccurate descriptions of the variables [30].…”

Section: Discussion Of the Findingsmentioning

confidence: 99%

Models and methods of regression analysis under conditions of fuzzy initial data

Раскін¹,

Сіра²,

Ivanchykhin³

2017

EEJET

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Розглянуто задачу регресійного аналізу з нечітко заданими змінними. Сформульовано та обґрунтовано критерій якості оцінки регресій-них коефіцієнтів, що враховує суттєві відмінно-сті у точності завдання змінних. Запропоновано метод розв'язання задачі. Розглянуто і виріше-но задачу нечіткої компараторної ідентифіка-ції, коли значення змінної, яка пояснюється, не визначено, але можуть бути ранжовані за змен-шенням будь-якого обраного показника Ключові слова: нечіткий регресійний ана-ліз, нечіткі вихідні дані, нечітка компараторна ідентифікація Рассмотрена задача регрессионного ана-лиза с нечётко заданными переменными. Сформулирован и обоснован критерий качества оценки регрессионных коэффициентов, учиты-вающий существенные различия в точности задания переменных. Предложен метод решения задачи. Рассмотрена и решена задача нечёткой компараторной идентификации, когда значе-ния объясняемой переменной не определены, но могут быть ранжированы по убыванию како-го-либо выбранного показателя Ключевые слова: нечёткий регрессионный анализ, нечёткие исходные данные, нечёткая компараторная идентификация UDC 519.24

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Section: Discussion Of the Findingsmentioning

confidence: 99%

Models and methods of regression analysis under conditions of fuzzy initial data

Раскін¹,

Сіра²,

Ivanchykhin³

2017

EEJET

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“…In real life, widespread incomplete information system makes the equivalence relation [7] no longer true. So the classical rough set theory is expanded, equivalence relation is relaxed to compatible relationship and similar relationship.…”

Section: Rough Set Theory and Attribute Reduction In Incomplete Imentioning

confidence: 99%

Bayesian Network Structure Learning and Its Applications in Intrusion Detection

Zhu¹,

Wang²,

Gao³

2015

Proceedings of the 2015 International Conference on Computer Science and Intelligent Communication

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Abstract-In this paper, an algorithm with attribute reduction based on rough set is introduced. The algorithm can effectively reduce the dimension of attributes and accurately determine the network structure using DBNI with the method of distribution in Bayesian network structure learning with incomplete data. The simulation result shows the algorithm can effectively improve the learning efficiency and detection accuracy of the network.

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“…Thus, in order to build a preference model from partly inconsistent preference data, we had an idea to structure this data using the concept of a rough set introduced by Pawlak (1982Pawlak ( , 1991. Since its conception, rough set theory has often proved to be an excellent mathematical tool for the analysis of inconsistent description of objects.…”

Section: Introductionmentioning

confidence: 99%

Rough set and rule-based multicriteria decision aiding

2012

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ABSTRACT. The aim of multicriteria decision aiding is to give the decision maker a recommendation concerning a set of objects evaluated from multiple points of view called criteria. Since a rational decision maker acts with respect to his/her value system, in order to recommend the most-preferred decision, one must identify decision maker's preferences. In this paper, we focus on preference discovery from data concerning some past decisions of the decision maker. We consider the preference model in the form of a set of "if..., then..." decision rules discovered from the data by inductive learning. To structure the data prior to induction of rules, we use the Dominance-based Rough Set Approach (DRSA). DRSA is a methodology for reasoning about data, which handles ordinal evaluations of objects on considered criteria and monotonic relationships between these evaluations and the decision. We review applications of DRSA to a large variety of multicriteria decision problems.

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Rough sets

Cited by 11,740 publications

References 2 publications

Models and methods of regression analysis under conditions of fuzzy initial data

Models and methods of regression analysis under conditions of fuzzy initial data

Bayesian Network Structure Learning and Its Applications in Intrusion Detection

Rough set and rule-based multicriteria decision aiding

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