Nguyen Long Giang¹, Tran Thanh Dai², Le Hoang Son³, Tran Thi Ngan⁴, Nguyen Nhu Son¹, Cu Nguyen Giap^5,*

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 3097-3124, 2024, DOI:10.32604/cmc.2024.057383 - 18 November 2024

Abstract Attribute reduction through the combined approach of Rough Sets (RS) and algebraic topology is an open research topic with significant potential for applications. Several research works have introduced a strong relationship between RS and topology spaces for the attribute reduction problem. However, the mentioned recent methods followed a strategy to construct a new measure for attribute selection. Meanwhile, the strategy for searching for the reduct is still to select each attribute and gradually add it to the reduct. Consequently, those methods tended to be inefficient for high-dimensional datasets. To overcome these challenges, we use the… More >

Xiaoqin Ma^1,2, Jun Wang¹, Wenchang Yu¹, Qinli Zhang^1,2,*

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2063-2083, 2024, DOI:10.32604/cmc.2024.049147 - 15 May 2024

Abstract The presence of numerous uncertainties in hybrid decision information systems (HDISs) renders attribute reduction a formidable task. Currently available attribute reduction algorithms, including those based on Pawlak attribute importance, Skowron discernibility matrix, and information entropy, struggle to effectively manages multiple uncertainties simultaneously in HDISs like the precise measurement of disparities between nominal attribute values, and attributes with fuzzy boundaries and abnormal values. In order to address the aforementioned issues, this paper delves into the study of attribute reduction within HDISs. First of all, a novel metric based on the decision attribute is introduced to solve… More >

Radwan Abu-Gdairi¹, R. Mareay^2,*, M. Badr³

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1025-1038, 2024, DOI:10.32604/cmc.2024.048647 - 25 April 2024

Abstract Recently, much interest has been given to multi-granulation rough sets (MGRS), and various types of MGRS models have been developed from different viewpoints. In this paper, we introduce two techniques for the classification of MGRS. Firstly, we generate multi-topologies from multi-relations defined in the universe. Hence, a novel approximation space is established by leveraging the underlying topological structure. The characteristics of the newly proposed approximation space are discussed. We introduce an algorithm for the reduction of multi-relations. Secondly, a new approach for the classification of MGRS based on neighborhood concepts is introduced. Finally, a real-life More >

Nadeem Salamat¹, Muhammad Kamran^1,2,*, Shahzaib Ashraf¹, Manal Elzain Mohammed Abdulla³, Rashad Ismail³, Mohammed M. Al-Shamiri³

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 1893-1933, 2024, DOI:10.32604/cmes.2023.044697 - 29 January 2024

Abstract The main goal of informal computing is to overcome the limitations of hypersensitivity to defects and uncertainty while maintaining a balance between high accuracy, accessibility, and cost-effectiveness. This paper investigates the potential applications of intuitionistic fuzzy sets (IFS) with rough sets in the context of sparse data. When it comes to capture uncertain information emanating from both upper and lower approximations, these intuitionistic fuzzy rough numbers (IFRNs) are superior to intuitionistic fuzzy sets and pythagorean fuzzy sets, respectively. We use rough sets in conjunction with IFSs to develop several fairly aggregation operators and analyze their… More > Graphic Abstract

Changshun Liu¹, Yan Liu¹, Jingjing Song^1,*, Taihua Xu^1,2

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2059-2075, 2023, DOI:10.32604/iasc.2023.039592 - 21 June 2023

Abstract Attribute reduction, as one of the essential applications of the rough set, has attracted extensive attention from scholars. Information granulation is a key step of attribute reduction, and its efficiency has a significant impact on the overall efficiency of attribute reduction. The information granulation of the existing neighborhood rough set models is usually a single layer, and the construction of each information granule needs to search all the samples in the universe, which is inefficient. To fill such gap, a new neighborhood rough set model is proposed, which aims to improve the efficiency of attribute… More >

Wenlong Ma^1,2,*, Youhong Xu¹, Jianwei Zheng², Sadaqat ur Rehman³

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1499-1512, 2023, DOI:10.32604/iasc.2023.030484 - 21 June 2023

Abstract In a cloud manufacturing environment with abundant functionally equivalent cloud services, users naturally desire the highest-quality service(s). Thus, a comprehensive measurement of quality of service (QoS) is needed. Optimizing the plethora of cloud services has thus become a top priority. Cloud service optimization is negatively affected by untrusted QoS data, which are inevitably provided by some users. To resolve these problems, this paper proposes a QoS-aware cloud service optimization model and establishes QoS-information awareness and quantification mechanisms. Untrusted data are assessed by an information correction method. The weights discovered by the variable precision Rough Set, More >

Tianshun Xing, Jianjun Chen^*, Taihua Xu, Yan Fan

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 561-581, 2023, DOI:10.32604/iasc.2023.037874 - 29 April 2023

Abstract It is well-known that attribute reduction is a crucial action of rough set. The significant characteristic of attribute reduction is that it can reduce the dimensions of data with clear semantic explanations. Normally, the learning performance of attributes in derived reduct is much more crucial. Since related measures of rough set dominate the whole process of identifying qualified attributes and deriving reduct, those measures may have a direct impact on the performance of selected attributes in reduct. However, most previous researches about attribute reduction take measures related to either supervised perspective or unsupervised perspective, which… More >

Rodyna A. Hosny¹, Radwan Abu-Gdairi², Mostafa K. El-Bably^3,*

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3073-3085, 2023, DOI:10.32604/iasc.2023.034234 - 15 March 2023

Abstract The theory of rough set represents a non-statistical methodology for analyzing ambiguity and imprecise information. It can be characterized by two crisp sets, named the upper and lower approximations that are used to determine the boundary region and accurate measure of any subset. This article endeavors to achieve the best approximation and the highest accuracy degree by using the minimal structure approximation space via ideal . The novel approach (indicated by ) modifies the approximation space to diminish the boundary region and enhance the measure of accuracy. The suggested method is more accurate than Pawlak’s More >

Ibrahim Noaman¹, Abd El Fattah El Atik², Tamer Medhat^3,*, Manal E. Ali⁴

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6893-6908, 2023, DOI:10.32604/cmc.2023.033539 - 28 December 2022

Abstract This article focuses on the relationship between mathematical morphology operations and rough sets, mainly based on the context of image retrieval and the basic image correspondence problem. Mathematical morphological procedures and set approximations in rough set theory have some clear parallels. Numerous initiatives have been made to connect rough sets with mathematical morphology. Numerous significant publications have been written in this field. Others attempt to show a direct connection between mathematical morphology and rough sets through relations, a pair of dual operations, and neighborhood systems. Rough sets are used to suggest a strategy to approximate… More >

Mohsen Eid¹, Tamer Medhat^2,*, Manal E. Ali³

Computer Systems Science and Engineering, Vol.45, No.2, pp. 1531-1544, 2023, DOI:10.32604/csse.2023.031745 - 03 November 2022

Abstract An information system is a type of knowledge representation, and attribute reduction is crucial in big data, machine learning, data mining, and intelligent systems. There are several ways for solving attribute reduction problems, but they all require a common categorization. The selection of features in most scientific studies is a challenge for the researcher. When working with huge datasets, selecting all available attributes is not an option because it frequently complicates the study and decreases performance. On the other side, neglecting some attributes might jeopardize data accuracy. In this case, rough set theory provides a… More >