A new mechanism of rule acquisition based on covering rough sets

Zhang, Xiaoping; Li, Jinjin; Li, Weikang

doi:10.1007/s10489-021-03067-x

Cited by 8 publications

(3 citation statements)

References 30 publications

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“…The rough set theory that Pawlak [37] put forward serves as a powerful mathematical technique for addressing information and knowledge that are imprecise, inconsistent, and incomplete without any assumptions and additional adjustments. Due to its innovative approach, distinct methodology, and straightforward operation, rough set theory has gained prominence in various fields such as intelligence information processing (e.g., [44,45]), pattern recognition (e.g., [46,47]), knowledge acquisition (e.g., [48]), and decision support analysis (e.g., [49]), among others. Note that this list is not intended to be comprehensive.…”

Section: Rough Set Theorymentioning

confidence: 99%

Preparedness Indicator System for Education 4.0 with FUCOM and Rough Sets

Almacen

Castilla

Gonzales

et al. 2023

Systems

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In view of the recent education sectoral transition to Education 4.0 (EDUC4), evaluating the preparedness of higher education institutions (HEIs) for EDUC4 implementation remains a gap in the current literature. Through a comprehensive review, seven criteria were evaluated, namely, human resources, infrastructure, financial, linkages, educational management, learners, and health and environment. This work offers two crucial contributions: (1) the development of an EDUC4 preparedness indicator system and (2) the design of a computational structure that evaluates each indicator and computes an aggregate preparedness level for an HEI. Using the full consistency method (FUCOM) to assign the priority weights of EDUC4 criteria and the rough set theory to capture the ambiguity and imprecision inherent in the measurement, this study offers an aggregate EDUC4 preparedness index to holistically capture the overall preparedness index of an HEI towards EDUC4. An actual case study is presented to demonstrate the applicability of the proposed indicator system. After a thorough evaluation, the results indicate that human resources were the most critical criterion, while health and environment ranked last. Insights obtained from the study provide HEIs with salient information necessary for decision making in various aspects, including the design of targeted policies and the allocation of resources conducive to implementing EDUC4 initiatives. The proposed indicator system can be a valuable tool to guide HEIs in pursuing EDUC4, resulting in a more effective and efficient implementation of this educational paradigm.

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Section: Rough Set Theorymentioning

confidence: 99%

Preparedness Indicator System for Education 4.0 with FUCOM and Rough Sets

Almacen

Castilla

Gonzales

et al. 2023

Systems

View full text Add to dashboard Cite

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“…Covering-based rough sets [12][13][14] were proposed to manage the type of covering data and have enriched Pawlak's rough sets in many ways, such as covering approximation models [15,16], covering reduction problems [17], and covering axiomatic systems [18]. Furthermore, they have been used in many real applications, such as decision rule synthesis [19,20], knowledge reduction [21,22], and other fields [23,24]. In theory, covering-based rough set theory has been connected with many theories, such as lattice theory [25,26], matroid theory [27,28], and fuzzy set theory [29][30][31].…”

Section: Introductionmentioning

confidence: 99%

Choquet-like Integrals with Multi-Neighborhood Approximation Numbers for Novel Covering Granular Reduction Methods

Wang,

Shao,

Zhang

2023

Mathematics

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Covering granular reduction is an important issue in multi-covering information systems. The main methods to solve this problem are set operators. How to solve this problem by quantitative analysis is an interesting topic. Furthermore, as a type of nonlinear fuzzy aggregation function (which is a quantitative tool), Choquet-like integrals with fuzzy measures are widely used in many files. However, the corresponding fuzzy measures in Choquet-like integrals are given by man, not by data. In this work, we present two types of multi-neighborhood approximation numbers in multi-covering information systems, which are used to establish Choquet-like integrals. Furthermore, they are applied to deal with the problem of granular reduction in multi-covering information systems. First, the notions of lower and upper multi-neighborhood approximation numbers are presented in a multi-covering information system, as well as their properties. Furthermore, some conditions under which multi-covering information systems induce the same lower and upper multi-neighborhood approximation numbers are presented. Second, two covering granular reduction methods based on multi-neighborhood approximation numbers are presented in multi-covering information systems. Third, multi-neighborhood approximation numbers are used to establish Choquet-like integrals, which are applied in covering granular reduction. Finally, these methods are compared with existing methods through experiments, which are used to demonstrate the effectiveness and benefits of our methods.

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“…In 1982, Pawlak [1,2] proposed rough set theory, as a mathematical tool, to deal with various kinds of data in data mining. It has been applied in various issues, such as attribute reduction [3][4][5], rule extraction [6][7][8], knowledge discovery [9][10][11] and feature selection [12][13][14]. To broaden the application ability of Pawlak's rough set theory in practical problems [11,15], it has been extended by generalized relations [16,17], various coverings [18][19][20] and several types of neighborhoods [4,21].…”

Section: Introductionmentioning

confidence: 99%

A Novel Multi-Criteria Decision-Making Method Based on Rough Sets and Fuzzy Measures

Wang

Zhang

2022

Axioms

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Rough set theory provides a useful tool for data analysis, data mining and decision making. For multi-criteria decision making (MCDM), rough sets are used to obtain decision rules by reducing attributes and objects. However, different reduction methods correspond to different rules, which will influence the decision result. To solve this problem, we propose a novel method for MCDM based on rough sets and a fuzzy measure in this paper. Firstly, a type of non-additive measure of attributes is presented by the importance degree in rough sets, which is a fuzzy measure and called an attribute measure. Secondly, for a decision information system, the notion of the matching degree between two objects is presented under an attribute. Thirdly, based on the notions of the attribute measure and matching degree, a Choquet integral is constructed. Moreover, a novel MCDM method is presented by the Choquet integral. Finally, the presented method is compared with other methods through a numerical example, which is used to illustrate the feasibility and effectiveness of our method.

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A new mechanism of rule acquisition based on covering rough sets

Cited by 8 publications

References 30 publications

Preparedness Indicator System for Education 4.0 with FUCOM and Rough Sets

Preparedness Indicator System for Education 4.0 with FUCOM and Rough Sets

Choquet-like Integrals with Multi-Neighborhood Approximation Numbers for Novel Covering Granular Reduction Methods

A Novel Multi-Criteria Decision-Making Method Based on Rough Sets and Fuzzy Measures

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