首页|Researchers' Work from Southwest University Focuses on Computational Intelligenc e (Feature Selection Using Generalized Multigranulation Dominance Neighborhood Rough Set Based On Weight Partition)
Researchers' Work from Southwest University Focuses on Computational Intelligenc e (Feature Selection Using Generalized Multigranulation Dominance Neighborhood Rough Set Based On Weight Partition)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators publish new report on Ma chine Learning - Computational Intelligence. According to news reporting out of Chongqing, People's Republic of China, by NewsRx editors, research stated, "Roug h set theory, as an academic hotspot in the field of artificial intelligence, ha s provided a solid theoretical foundation for feature selection. However, with t he continuous updating of large datasets, classical rough set theory is no longe r applicable." Financial support for this research came from National Natural Science Foundatio n of China (NSFC). Our news journalists obtained a quote from the research from Southwest Universit y, "Multi-granulation rough set theory is an extension of rough set theory that can better handle complex datasets. Therefore, this paper proposes a generalized multi-granulation dominance neighborhood rough set model based on weight distri bution and discusses some relevant properties of this model. Furthermore, a new information entropy is constructed based on this model to handle uncertainty in data. This approach enhances the ability to describe uncertainty and enables mor e effective feature selection. As a result, a forward heuristic feature selectio n algorithm is developed to find the optimal feature subset."
ChongqingPeople's Republic of ChinaAsiaComputational IntelligenceMachine LearningSouthwest University
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