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一种基于贪婪算法的KNN参数选择策略 被引量：2

A Strategy for Parameter Selecting in KNN Based on Greedy Search
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摘要 K近邻算法是基于向量空间模型的最好的文本分类算法之一。使用KNN算法时通常要用贪婪算法进行参数选择,最终的参数不仅取决于每个参数的初始值及候选值,而且和参数选择的顺序密切相关。不同的参数选择策略间存在较大差异,通过实验,指出了KNN算法进行文本分类时一个较好的参数选择策略。 KNN（K nearest neighbors） is one of the best text categorization algorithms based on Vector Space Model. Greedy Algorithm is the most common parameter selecting method for KNN,the final result depends on not only the initial parameter and the candidate parameters but also the order in the tuning process. This survey introduces a better strategy for parameter selecting in KNN by experiment.
作者 金自翔 戴新宇 陈家骏
机构地区 南京大学计算机软件新技术国家重点实验室
出处 《广西师范大学学报（自然科学版）》 CAS 北大核心 2008年第1期182-185,共4页 Journal of Guangxi Normal University:Natural Science Edition
基金 国家863计划基金资助项目(2006AA01Z143,2006AA01Z139) 国家社会科学基金资助项目(07BYY051) 江苏省自然科学基金资助项目(BK2006117)
关键词 文本分类 K近邻 参数调节 贪婪算法 text categorization KNN parameter tuning greedy search
分类号 TP181 [自动化与计算机技术—控制理论与控制工程]
  • 相关文献

参考文献11

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共引文献70

同被引文献21

  • 1ZHANG Shi-chao.Parimputation:from imputation and null-imputation to partially imputation[J].IEEE Intelligent Informatics Bulletin,2008,9 (1):32-38.
  • 2ZHANG Shi-chao.Shell-neighbor method and its application in missing data imputation[J].Applied Intelligence,2010(待发).
  • 3QIN Yong-song,ZHANG Shi-chao,ZHU Xiao-feng,et al.Semi-parametric optimization for missing data imputation[J].Applied Intelligence,2007,27 (1):79-88.
  • 4BATISTA G,MONARD M C.An analysis of four missing data treatment methods for supervised learning[J].Applied Artificial Intelligence,2003,17 (5):519-533.
  • 5GEDIGA G,DUNTSCH I.Maximum consistency of incomplete data via non-invasive imputation[J].Artificial Intelligence Review,2003,19 (1):93-107.
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  • 8朱晓锋.缺失值填充若干问题研究[D].桂林:广西师范大学计算机科学与信息工程学院,2007.
  • 9周念成,廖建权,王强钢,李春艳,李剑.深度学习在智能电网中的应用现状分析与展望[J].电力系统自动化,2019,43(4):180-191. 被引量：190
  • 10黄景光,丁婧,郑淑文,林湘宁.基于电流突变量的自适应过电流保护新原理[J].电力系统保护与控制,2018,46(7):49-55. 被引量：15

引证文献2

二级引证文献5

广西师范大学学报（自然科学版）
2008年 第1期

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