Valid Incremental Attribute Reduction Algorithm Based on Attribute Generalization for an Incomplete Information System

Guang-qiong, Wang

doi:10.1049/cje.2019.05.014

Cited by 3 publications

(2 citation statements)

References 39 publications

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“…After 16 weeks of experiment, it was concluded that the exercise could significantly improve the ankle muscle strength, proprioception, and dynamic balance ability of lower limbs of FAI students. Wang's [ 20 ] research shows that the excitement of the central nervous system of human body is a slow process, and the physiological inertia of internal organs also exists. In order to adapt to the relatively intense and intense basketball, it is necessary to carry out sufficient preparation activities to slowly mobilize the excitement of the nervous system and gradually overcome the physiological inertia of various internal organs.…”

Section: Related Workmentioning

confidence: 99%

Early Warning of Basketball Injury Risk Based on Attribute Reduction Algorithm

Wang

Seung-Soo

2022

Computational Intelligence and Neuroscience

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Basketball is one of the students’ favorite ball games, and it is also one of the most popular sports for college students to carry out after class. Especially in recent years, with the spread of NBA culture around the world and the extensive development of CUBA in domestic colleges and universities, Yao Ming has appeared in China to compete in the NBA and achieve brilliant achievements. With the increasing investment in the number of basketball venues, basketball itself, as a sport with low dependence on venues, equipment, and people, has the characteristics of economy, convenience, and remarkable sports effect compared with other sports. College students’ basketball skills, basketball awareness, and love for basketball are increasing day by day. Attribute reduction algorithm is one of the core contents of knowledge discovery, which describes whether every attribute in the attribute set of information system is necessary and how to delete unnecessary knowledge. Based on the attribute reduction algorithm, this paper studies the early warning of basketball injury risk. The basketball injury can not only make athletes unable to participate in training or competition, but even cripple or lose their lives, which hinders the normal development of sports. Therefore, we should make a comprehensive and objective analysis of sports training to find out the causes of sports injuries, so as to prevent sports injuries. This algorithm takes the attribute frequency as the heuristic information and solves the attribute selection problem when the attribute frequency is the same.

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Section: Related Workmentioning

confidence: 99%

Early Warning of Basketball Injury Risk Based on Attribute Reduction Algorithm

Wang

Seung-Soo

2022

Computational Intelligence and Neuroscience

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“…The incremental attribute reduction method has garnered a great deal of interest because it may efficiently use the reduction results that have already been achieved, saving a significant amount of time and space [8]. There have been numerous studies on incremental rough set theory, e.g., in feature selection [63][64][65][66], incremental approximation calculation [67], incremental information [68], rule discovery [69], and case-based reasoning [70], where the incremental technique allows new data to be added without re-implementing the algorithm in a dynamic database. Nevertheless, the majority of rough set approaches fail to take into consideration the problem of many result attributes, making them ineffective and ill-suited to understanding the nature of the rules in the big data era.…”

Section: Rule Induction Based On Rough Setsmentioning

confidence: 99%

Incremental Green Investment Rule Induction Using Intelligent Rough Sets from an Energy Perspective

Huang,

Liang,

Chuang

et al. 2024

Sustainability

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The United Nations Sustainable Development Goals (SDGs) agenda has stated the importance of green investment. Energy-related green investment involves intricate economic behavior and ecological objectives. Green investment definitely requires agile decisions, e.g., rule-based decisions, to respond to changes outside the country. The identification of significant rules with numerous result features and the assurance of the stability and robustness of the rules in decision-making are crucial for green energy investment. The rough set (RS) methodology works well for processing qualitative data that are difficult to examine with traditional statistical methods in order to induce decision rules. The RS methodology starts with the analysis of the limits of discernibility of a subset of objects belonging to the domain to induce rules. However, traditional RS methods cannot incrementally generate rules with outcome features when new objects are added, which frequently occurs in green energy investment with the inclusion of big data. In this paper, an intelligent RS approach is proposed. This approach effectively identifies the rules that either stay the same or are altered based on four classified cases after a new object is introduced; it is novel because it can deal with a complicated investment environment by imposing multiple outcome features, specifically when it is required to flexibly extract new decision rules via adding new data sets.

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Collaborative profit allocation schemes for logistics enterprise coalitions with incomplete information

Liu

Sheu

et al. 2021

Omega

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Valid Incremental Attribute Reduction Algorithm Based on Attribute Generalization for an Incomplete Information System

Cited by 3 publications

References 39 publications

Early Warning of Basketball Injury Risk Based on Attribute Reduction Algorithm

Early Warning of Basketball Injury Risk Based on Attribute Reduction Algorithm

Incremental Green Investment Rule Induction Using Intelligent Rough Sets from an Energy Perspective

Collaborative profit allocation schemes for logistics enterprise coalitions with incomplete information

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