Distributed approach for computing rough set approximations of big incomplete information systems

Hamed, Ahmed Abdeen; Sobhy, Ahmed; Nassar, Hamed

doi:10.1016/j.ins.2020.08.049

Cited by 24 publications

(13 citation statements)

References 30 publications

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“…The optimization provided by data mining technology can provide reference for optimization of teaching design and teaching methods (Gao, 2020). Therefore, data mining technology provides the guarantee for basic information from the perspective of teaching decision-making so as to support optimization decision for blended teaching and learning (Hamed et al, 2021).…”

Section: Optimization Of Blended Learning Model With Data Mining Algo...mentioning

confidence: 99%

“…Since then, the research and application of autonomous learning in foreign language teaching has been increasing. Some scholars focus on exploring the meanings of autonomous learning (Benson, 2007; Peeters, 2018); some scholars focus on exploring the teaching practice of autonomous learning (Macaskill & Denovan, 2013; Oxford, 2015), and the role of teachers’ play in teaching (Hamed et al, 2021; Lai et al, 2016; Rosell-Aguilar, 2018); some scholars are dedicated to exploring learning strategies related to foreign language autonomous learning (Cockrum, 2014; Oxford, 2015). Besides, some scholars believe that autonomous learning is rooted in western theories and is committed to exploring the cultural adaptability of foreign language autonomous learning in other environments (Schmid, 2018).…”

Section: Autonomous Learning Behavior and Blended Learningmentioning

confidence: 99%

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Improvement of Students’ Autonomous Learning Behavior by Optimizing Foreign Language Blended Learning Mode

Wang

Zhang

2022

Sage Open

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Given the significance of cultivating students’ autonomous learning ability, there is a need to develop an instructional model that can improve students’ awareness and behavior of autonomous learning, as well as to explore the effectiveness and optimization of this model effectively. Taking college English course as a case study, this paper constructs a blended learning mode based on SPOC, which combines advantages of online and offline teaching. 15 types of nonredundant sets resulting from 500 questionnaires has been explored, and the optimal factor combinations have been found out from 15 types with the technology of data mining to optimize the mode constructed previously. Optimized blended learning mode, emphasizing the optimal factors more, has been applied to College English curriculum design and teaching practice in China. Surveys of students’ achievement and autonomous learning behavior have been conducted after experiment. The results of the research indicate that the optimized blended learning mode will stimulate foreign language learners’ learning motivation, cultivate their autonomous learning ability, so as to construct and improve their autonomous learning behavior further.

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Section: Optimization Of Blended Learning Model With Data Mining Algo...mentioning

confidence: 99%

Section: Autonomous Learning Behavior and Blended Learningmentioning

confidence: 99%

Improvement of Students’ Autonomous Learning Behavior by Optimizing Foreign Language Blended Learning Mode

Wang

Zhang

2022

Sage Open

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“…The presence of both types in the information complicates processing. What is more, this information can also be incomplete, in the sense that some features may have missing values [5]. The value of a feature can be missing due to several factors, such as negligence, cost, or difficulty to obtain.…”

Section: Introductionmentioning

confidence: 99%

Accurate Classification of COVID-19 Based on Incomplete Heterogeneous Data using a KNN Variant Algorithm

2021

Self Cite

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Great efforts are now underway to control the coronavirus 2019 disease (COVID-19). Millions of people are medically examined, and their data keep piling up awaiting classification. The data are typically both incomplete and heterogeneous which hampers classical classification algorithms. Some researchers have recently modified the popular K NN algorithm as a solution, where they handle incompleteness by imputation and heterogeneity by converting categorical data into numbers. In this article, we introduce a novel K NN variant (K NNV) algorithm that provides better results as demonstrated by thorough experimental work. We employ rough set theoretic techniques to handle both incompleteness and heterogeneity, as well as to find an ideal value for K. The K NNV algorithm takes an incomplete, heterogeneous dataset, containing medical records of people, and identifies those cases with COVID-19. We use in the process two popular distance metrics, Euclidean and Mahalanobis, in an effort to widen the operational scope. The K NNV algorithm is implemented and tested on a real dataset from the Italian Society of Medical and Interventional Radiology. The experimental results show that it can efficiently and accurately classify COVID-19 cases. It is also compared to three K NN derivatives. The comparison results show that it greatly outperforms all its competitors in terms of four metrics: precision, recall, accuracy, and F-Score. The algorithm given in this article can be easily applied to classify other diseases. Moreover, its methodology can be further extended to do general classification tasks outside the medical field.

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“…incomplete numerical data. Hamed et al [13] studied big incomplete information systems in rough set model by distributed approach. Dai et al [14] introduced a new form of conditional entropy to measure the importance of attributes in incomplete decision systems.…”

Section: Introductionmentioning

confidence: 99%

Semi-supervised attribute reduction for interval data based on misclassification cost

Dai

Liu

2021

Int. J. Mach. Learn. & Cyber.

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Attribute reduction is a key issue in rough set theory which is widely used to handle uncertain knowledge. In reality, partially labeled interval data exist widely. So far, there are very few studies on partially labeled interval information systems. In this paper, we first define the concept of interval neighborhood by means of Kullback-Leibler (KL) divergence and standard deviation. Then a method is proposed to estimate the missing label by the nearest labeled objects to an unlabeled object and the cost of misclassification is constructed. Next a new entropy structure based on misclassification cost is proposed. After that, a semi-supervised attribute reduction method for partially labeled interval data is advanced. Finally, The rationality and validity of the method are demonstrated by experimental comparison.

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Distributed approach for computing rough set approximations of big incomplete information systems

Cited by 24 publications

References 30 publications

Improvement of Students’ Autonomous Learning Behavior by Optimizing Foreign Language Blended Learning Mode

Improvement of Students’ Autonomous Learning Behavior by Optimizing Foreign Language Blended Learning Mode

Accurate Classification of COVID-19 Based on Incomplete Heterogeneous Data using a KNN Variant Algorithm

Semi-supervised attribute reduction for interval data based on misclassification cost

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