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    Applying Feature-Weighted Gradient Decent K-Nearest Neighbor to Select Promising Projects for Scientific Funding

    Chuqing Zhang1, Jiangyuan Yao2, *, Guangwu Hu3, Thomas Schøtt4

    CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1741-1753, 2020, DOI:10.32604/cmc.2020.010306

    Abstract Due to its outstanding ability in processing large quantity and high-dimensional data, machine learning models have been used in many cases, such as pattern recognition, classification, spam filtering, data mining and forecasting. As an outstanding machine learning algorithm, K-Nearest Neighbor (KNN) has been widely used in different situations, yet in selecting qualified applicants for winning a funding is almost new. The major problem lies in how to accurately determine the importance of attributes. In this paper, we propose a Feature-weighted Gradient Decent K-Nearest Neighbor (FGDKNN) method to classify funding applicants in to two types: approved ones or not approved ones.… More >

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