Rough sets and topological spaces based on similarity

Abo-Tabl, E. A.

doi:10.1007/s13042-012-0107-7

Cited by 44 publications

(24 citation statements)

References 36 publications

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“…This method is utilized in conjunction with other learning approaches having explanation capability that learn what HELM has learned. This study adopts the rough set theory (RST) [37] as the knowledge generator, because it has numerous advantages: (1) it yields a human-readable representation in an ''if (condition), then (decision)'' style [14,34]; (2) it handles vagueness and uncertainty in decision making [1,20,28,45,47,48,53]; (3) it is grounded only on the initial data and does not require additional information, unlike the grade of membership in the fuzzy set theory or probability in statistics [18,43]; and (4) the informative rules induced from RST are based on facts, because each decision rule is supported by a set of real-life examples [17]. The knowledge generated from HELM by RST can be viewed as a roadmap for decision makers to make reliable judgments.…”

Section: Hybrid Ensemble Learning Forecasting Mechanism (Helm)mentioning

confidence: 99%

An emerging hybrid mechanism for information disclosure forecasting

Hsu

Lin

2014

Int. J. Mach. Learn. & Cyber.

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Corporate governance mechanisms ensure that investors get a fair return on their investment. A wellestablished governance mechanism reduces the information asymmetry and agency cost between a firm's management and stakeholders, but decision makers find it difficult to assess the corporate governance status of publicly-listed firms before the annual official announcement the following year. This study proposes a hybrid ensemble learning forecasting mechanism (HELM), whose single-component candidates from the extreme learning machine (ELM) algorithm with dissimilar ensemble strategies (that is, data diversity, parameter diversity, kernel diversity, and preprocessing diversity) form one initial dataset. We implement locally linear embedding into the proposed mechanism to handle the dimensionality task and then utilize the weighted voting taken from the base components' crossvalidation performance on a training dataset as the integration mechanism. Experimental results show that the proposed HELM significantly outperforms the other classifiers, but its superior performance under many real-life application domains comes with a critical drawback: it is incapable of providing an explanation for the underlying reasoning mechanisms. Thus, this study advances the utilized rough set theory with its explanation capability to extract the inherent knowledge from the ensemble mechanism (HELM). The informative rules can be used as a guideline for decision makers to make a reliable judgment under turbulent financial markets.

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Section: Hybrid Ensemble Learning Forecasting Mechanism (Helm)mentioning

confidence: 99%

An emerging hybrid mechanism for information disclosure forecasting

Hsu

Lin

2014

Int. J. Mach. Learn. & Cyber.

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“…e interaction between topological and rough set theory is due to Skowron [3] and Wiweger [4] who first discussed the role of topological aspects in rough sets. en, a combination of rough set theory and topological theory became the main goal of many studies [5][6][7][8][9][10][11]. is interaction also included some generalizations of topology such as minimal structure [12].…”

Section: Introductionmentioning

confidence: 99%

Various Topologies Generated from E_j‐Neighbourhoods via Ideals

et al. 2021

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One of the considerable subjects in mathematics is the study of topology. Deducing topology from arbitrary binary relations has enticed the attention of many researchers. So, we devote this article to generate some kinds of topologies from ideals and E j -neighborhoods which are induced from any binary relation. We define new types of approximations and accuracy measures from these topologies and then compare them with their counterparts induced directly from E j -neighborhoods and ideals. Also, we show that the approximations and accuracy measures given, herein, are better than those introduced in some previous studies under any arbitrary relation.

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“…Traditional rough approaches are based on equivalence relations, but this condition is not fulfilled in some cases. So, the approximations have been broaden to the similarity relation based rough sets [2], [3], the tolerance relation based rough sets [4], the dominance relation based rough sets [5], the arbitrary binary relation based rough sets [6], [7], [9]. An attractive and inherent research aim in rough set theory is to study rough set theory by means of topology.…”

Section: Introductionmentioning

confidence: 99%

Covering-Based Rough Fuzzy, Intuitionistic Fuzzy and Neutrosophic Nano Topology and Applications

et al. 2019

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In recent years, ''mathematical orientations on real-life problems'', which continue to increase, began to make a significant impact. Information systems for many decision-making problems consist of uncertain, incomplete, indeterminate and indiscernible structures and components. Classical set theory and interpretation methods fail to represent, express and solve the problems of these types or cause to make wrong decisions. For this reason, in this study, we provide definitions and methods to present information and problem representations in more detail and precision. This paper introduces three new topologies called covering-based rough fuzzy, covering-based rough intuitionistic fuzzy and covering-based rough neutrosophic nano topology. Some fundamental definitions such as open set, closed set, interior, closure and basis are given. Neutrosophic definitions and properties are mainly investigated. We give some real life applications of covering-based rough neutrosophic nano topology in the final part of the paper and an explanatory example of decision making application by defining core point.

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Rough sets and topological spaces based on similarity

Cited by 44 publications

References 36 publications

An emerging hybrid mechanism for information disclosure forecasting

An emerging hybrid mechanism for information disclosure forecasting

Various Topologies Generated from E_j‐Neighbourhoods via Ideals

Covering-Based Rough Fuzzy, Intuitionistic Fuzzy and Neutrosophic Nano Topology and Applications

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Rough sets and topological spaces based on similarity

Cited by 44 publications

References 36 publications

An emerging hybrid mechanism for information disclosure forecasting

An emerging hybrid mechanism for information disclosure forecasting

Various Topologies Generated from Ej‐Neighbourhoods via Ideals

Covering-Based Rough Fuzzy, Intuitionistic Fuzzy and Neutrosophic Nano Topology and Applications

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Product

Resources

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Various Topologies Generated from E_j‐Neighbourhoods via Ideals