A survey on granular computing and its uncertainty measure from the perspective of rough set theory

Cheng, Yunlong; Zhao, Fan; Zhang, Qinghua; Wang, Guoyin

doi:10.1007/s41066-019-00204-3

Cited by 20 publications

(4 citation statements)

References 133 publications

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“…Rough set theory is a mathematical tool proposed by Pawlak to deal with fuzzy and uncertain knowledge [35]. It has been successfully applied in machine learning, smart system, decision analysis, knowledge discovery, and data mining [36][37][38].…”

Section: Rough Setmentioning

confidence: 99%

AI-Based Employment Situation Analysis of Innovative Talents in the New Media Era

Ma¹

2023

J Multimed Inf Syst

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As the number of undergraduates increases, competition for employment opportunities becomes more intense. To improve employment guidance and increase the employment rate and quality, it is essential to evaluate preliminary employment data, identify the primary variables that influence employment, and provide decision support for decision-makers. In the era of new media, the influx of massive information can make it difficult for undergraduates to distinguish between true and false information, making it necessary to analyze the employment situation of innovative talents using artificial intelligence. However, historical employment data is generally a dataset with high noise due to the diversity and complexity of actual employment situations. Differences in accuracy requirements among various institutions also make handling issues such as different accuracy requirements and noise adaptation abilities challenging. To address this issue, this study proposes applying a decision tree algorithm based on a multi-scale rough set model to analyze university employment data. By introducing the multiscale concept into rough set theory and drawing on variable precision rough sets, the analysis's accuracy and noise adaptation ability can be improved. Experimental results show that the proposed algorithm performs well in terms of classification accuracy and running time, and the employment situation of creative minds can be effectively analyzed. Moreover, the decision tree has a simple structure, concise rules, no inseparable datasets, and a fast running speed.

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Section: Rough Setmentioning

confidence: 99%

AI-Based Employment Situation Analysis of Innovative Talents in the New Media Era

Ma¹

2023

J Multimed Inf Syst

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“…The rough set theory (RST) technique, which was introduced by [26], comes from simple model information. RST is considered a new mathematical tool for processing uncertain information after the uncertainty theory [27] and fuzzy set theory [28] for data analysis and data processing. RST has been identified as an effective mathematical method of minimizing data from information systems as well as a mathematical tool for solving uncertain problems [29].…”

Section: Related Studiesmentioning

confidence: 99%

Optimization of Sentiment Analysis Using Teaching-Learning Based Algorithm

Abdullah¹,

Abdullah²,

Sani³

2021

Computers, Materials &Amp; Continua

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Feature selection and sentiment analysis are two common studies that are currently being conducted; consistent with the advancements in computing and growing the use of social media. High dimensional or large feature sets is a key issue in sentiment analysis as it can decrease the accuracy of sentiment classification and make it difficult to obtain the optimal subset of the features. Furthermore, most reviews from social media carry a lot of noise and irrelevant information. Therefore, this study proposes a new text-feature selection method that uses a combination of rough set theory (RST) and teaching-learning based optimization (TLBO), which is known as RSTLBO. The framework to develop the proposed RSTLBO includes numerous stages:(1) acquiring the standard datasets (user reviews of six major U.S. airlines) which are used to validate search result feature selection methods, (2) preprocessing of the dataset using text processing methods. This involves applying text processing methods from natural language processing techniques, combined with linguistic processing techniques to produce high classification results, (3) employing the RSTLBO method, and (4) using the selected features from the previous process for sentiment classification using the Support Vector Machine (SVM) technique. Results show an improvement in sentiment analysis when combining natural language processing with linguistic processing for text processing. More importantly, the proposed RSTLBO feature selection algorithm is able to produce an improved sentiment analysis.

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“…How to deal with uncertain and imprecise information in decision making has emerged as a prominent and important topic, which has gained much attention [1][2][3]. At present, to solve this problem, a plenty of theories have developed, including fuzzy set theory [4][5][6], neutrosophic set theory [7,8], intuitionistic fuzzy set [9,10], fermatean fuzzy set [11,12]], N-soft [13,14], Dempster-Shafer evidence theory [15][16][17], rough set theory [18,19].…”

Section: Introductionmentioning

confidence: 99%

A Belief Similarity Measure for Dempster-Shafer Evidence Theory and Application in Decision Making

Liu

2024

J. Soft. Comput. Decis. Anal.

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How to effectively deal with uncertain and imprecise information in decision making is a complex task. Dempster-Shafer evidence theory (DSET) is widely used for handling such challenges due to its ability to model uncertainty and imprecision. However, Dempster's rule can sometimes yield counterintuitive results when dealing with highly conflicting evidence. In this paper, we introduce a novel belief sine similarity measure, called $BS^2M$, which effectively measures the discrepancy between different pieces of evidence. We also establish that $BS^2M$ possesses important properties such as boundedness, symmetry, and non-degeneracy. Building upon $BS^2M$, we present a new method for decision making. The proposed method considers both the credibility and the information volume of each evidence, providing a more comprehensive reflection of their importance. To validate our method, we conduct experiment in target recognition application, demonstrating the effectiveness and rationality of the proposed method.

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A survey on granular computing and its uncertainty measure from the perspective of rough set theory

Cited by 20 publications

References 133 publications

AI-Based Employment Situation Analysis of Innovative Talents in the New Media Era

AI-Based Employment Situation Analysis of Innovative Talents in the New Media Era

Optimization of Sentiment Analysis Using Teaching-Learning Based Algorithm

A Belief Similarity Measure for Dempster-Shafer Evidence Theory and Application in Decision Making

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