首页|Researchers at Zhejiang Normal University Release New Data on Machine Learning ( An Improved Binary Dandelion Algorithm Using Sine Cosine Operator and Restart St rategy for Feature Selection)
Researchers at Zhejiang Normal University Release New Data on Machine Learning ( An Improved Binary Dandelion Algorithm Using Sine Cosine Operator and Restart St rategy for Feature Selection)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Researchers detail new data in Machine Learning. According to news reporting from Jinhua,People's Republic of China, by NewsRx j ournalists, research stated, "Feature selection (FS) is an important data prepro cessing technology for machine learning and data mining. Metaheuristic algorithm (MH) has been widely used in feature selection because of its powerful search f unction." Financial support for this research came from National Natural Science Foundatio n of China (NSFC). The news correspondents obtained a quote from the research from Zhejiang Normal University, "This paper presents an improved Binary Dandelion Algorithm using Si ne Cosine operator and Restart strategy (SCRBDA) for feature selection. First, t he sine cosine operator is used in the radius formula of the core dandelions (CD ), which significantly enhances the ability of algorithm development and explora tion. Secondly, the algorithm uses a restart strategy to increase its ability to get rid of local optimum. Thirdly, mutual information is used to guide the gene ration of some dandelions, which pays more attention to the correlation between the selected features and categories. Finally, quick bit mutation is used as the mutation strategy to improve the diversity of the population. The SCRBDA propos ed in this paper was tested on 18 datasets of different sizes from UCI machine l earning database. The SCRBDA was compared with 8 other classical feature selecti on algorithms, and the performance of the proposed algorithm was evaluated throu gh feature subset size, classification accuracy, fitness value, and F1-score. Th e experimental results show that SCRBDA achieves the best performance, which has stronger feature reduction ability and achieves better overall performance on m ost datasets."
JinhuaPeople's Republic of ChinaAsiaAlgorithmsCyborgsEmerging TechnologiesMachine LearningZhejiang Normal University
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