An incremental attribute reduction approach based on knowledge granularity with a multi-granulation view

Jing, Yunge; Li, Tianrui; Fujita, Hamido; Zeng, Yu; Wang, Bin

doi:10.1016/j.ins.2017.05.003

Cited by 95 publications

(17 citation statements)

References 38 publications

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“…In recent years, the research on rough set theory in the field of artificial intelligence has been a hot topic, and this theory has been widely used. Knowledge reduction [23][24][25] is one of the core concepts of rough set theory.…”

Section: Rough Set Neural Networkmentioning

confidence: 99%

Rough Set Neural Network Feature Extraction and Pattern Recognition of Shaft Orbits Based on the Zernike Moment

Zhang

Zhou

et al. 2021

Shock and Vibration

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In the shaft axis monitoring of hydrogenerating unit condition monitoring and fault diagnosis, the shaft orbit is intuitive and comprehensively reflects the unit operation state, and different shaft orbits correspond to different fault types, which can accurately indicate a system vibration fault. Shaft orbit identification has important significance for vibration fault diagnosis. In getting the feature extraction and pattern recognition of a shaft orbit, the Zernike moment is better than the Hu moment; it has the advantages of a small calculation error and a high recognition rate. A rough set neural network (RS-BP hybrid model) of shaft orbit recognition is established, which uses just 13 moment eigenvalues reserved by the rough set feature selection algorithm as input variables; it has the same calculation error and recognition rate and reduces the calculation time step. The simulation of the recognition of shaft orbits shows that the hybrid model has achieved good results in the identification of shaft orbits.

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Section: Rough Set Neural Networkmentioning

confidence: 99%

Rough Set Neural Network Feature Extraction and Pattern Recognition of Shaft Orbits Based on the Zernike Moment

Zhang

Zhou

et al. 2021

Shock and Vibration

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show abstract

“…Given the limitations of manual coding techniques in large-scale exploratory text analyses ( Kobayashi et al., 2018 ), the discovery of latent knowledge patterns requires examining thematic clusters by means of text mining techniques, which make it possible to automatically reduce dimensionality by filtering quality information from high-dimensional sets of textual data. The core knowledge embedded in a large-scale dataset can be expressed as the sum of three complementary sub-information systems ( Jing et al., 2017 ) of latent variables: main keywords, central topics, and core research themes 1 Sourcing and connecting the different levels of knowledge which are rooted in these subsystems is key to produce a condensed but thorough representation of the intellectual shape of a research area. As a result, a comprehensive knowledge discovery process entails a multi-granulation perspective ( Roslovtsev and Marenkov, 2018 ; Thijs, 2019 ).…”

Section: Latent Knowledge Discovery In Av Research: a Multi-granulatimentioning

confidence: 99%

Mind the gap: Developments in autonomous driving research and the sustainability challenge

Mora

Guo

Panori³

2020

Journal of Cleaner Production

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Scientific knowledge on autonomous-driving technology is expanding at a faster-than-ever pace. As a result, the likelihood of incurring information overload is particularly notable for researchers, who can struggle to overcome the gap between information processing requirements and information processing capacity. We address this issue by adopting a multi-granulation approach to latent knowledge discovery and synthesis in large-scale research domains. The proposed methodology combines citation-based community detection methods and topic modelling techniques to give a concise but comprehensive overview of how the autonomous vehicle (AV) research field is conceptually structured. Thirteen core thematic areas are extracted and presented by mining the large data-rich environments resulting from 50 years of AV research. The analysis demonstrates that this research field is strongly oriented towards examining the technological developments needed to enable the widespread rollout of AVs, whereas it largely overlooks the wide-ranging sustainability implications of this sociotechnical transition. On account of these findings, we call for a broader engagement of AV researchers with the sustainability concept and we invite them to increase their commitment to conducting systematic investigations into the sustainability of AV deployment. Sustainability research is urgently required to produce an evidence-based understanding of what new sociotechnical arrangements are needed to ensure that the systemic technological change introduced by AV-based transport systems can fulfill societal functions while meeting the urgent need for more sustainable transport solutions.

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“…With the development of rough set theory, Zadeh [ 37 ] proposed the concept of knowledge granularity in 1996, which is a theory that uses granularity in the process of solving problems. Knowledge granularity is an important part of artificial intelligence and information processing [ 38 , 39 , 40 ]. A knowledge granule is a group of objects gathered together through the indiscernibility, similarity, and proximity of attributes [ 41 , 42 ].…”

Section: Theoretical Backgroundmentioning

confidence: 99%

An Efficient Alert Aggregation Method Based on Conditional Rough Entropy and Knowledge Granularity

Sun

Chen

2020

Entropy

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With the emergence of network security issues, various security devices that generate a large number of logs and alerts are widely used. This paper proposes an alert aggregation scheme that is based on conditional rough entropy and knowledge granularity to solve the problem of repetitive and redundant alert information in network security devices. Firstly, we use conditional rough entropy and knowledge granularity to determine the attribute weights. This method can determine the different important attributes and their weights for different types of attacks. We can calculate the similarity value of two alerts by weighting based on the results of attribute weighting. Subsequently, the sliding time window method is used to aggregate the alerts whose similarity value is larger than a threshold, which is set to reduce the redundant alerts. Finally, the proposed scheme is applied to the CIC-IDS 2018 dataset and the DARPA 98 dataset. The experimental results show that this method can effectively reduce the redundant alerts and improve the efficiency of data processing, thus providing accurate and concise data for the next stage of alert fusion and analysis.

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An incremental attribute reduction approach based on knowledge granularity with a multi-granulation view

Cited by 95 publications

References 38 publications

Rough Set Neural Network Feature Extraction and Pattern Recognition of Shaft Orbits Based on the Zernike Moment

Rough Set Neural Network Feature Extraction and Pattern Recognition of Shaft Orbits Based on the Zernike Moment

Mind the gap: Developments in autonomous driving research and the sustainability challenge

An Efficient Alert Aggregation Method Based on Conditional Rough Entropy and Knowledge Granularity

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