期刊文献+

分类问题中的特征选取

Feature Selection in Classification Problem
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摘要 针对分类问题提出了一种特征选择的新算法.算法在初始化时首先任意产生几个特征集,然后被迭代多次执行,在每一次迭代过程中,用性能评估函数对所有的特征集进行评价,按照评价结果选取当前性能最优的特征集,其它所有的特征集朝着类似当前最优特征集的方向发生变化,直至完成预定的次数为止. According to the classification problem, a new algorithm of feature selection is proposed. First of all, several feature sets randomly are produced when the algorithm is initialized, then it will be iteratived times. All the feature sets are evaluated by using the performance evaluation function in each generation. The optimal current feature set is selected according to the evaluation results, and all the other feature sets change towards the similar direction of the optimal current feature set until the algorithm is performed in pre-defined times.
出处 《鲁东大学学报(自然科学版)》 2014年第3期219-222,共4页 Journal of Ludong University:Natural Science Edition
关键词 特征选取 分类问题 迭代 评估函数 feature selection classification problem iteration evaluation function
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参考文献8

  • 1Zadeh L A. Fuzzy sets[J].Information and Control,1965,(03):338-353.
  • 2Pawlak Z. Rough sets[J].International Journal of Computer and Informaiton Sciences,1982,(05):341-356.
  • 3Pawlak Z. Rough Sets:Theoretical Aspects of Reasoning about Data[M].Dordrecht:Kluwer Academic Publishers,1991.
  • 4王国胤.Rough集理论与知识获取[M]西安:西安交通大学出版社,2001.
  • 5(S)lezak D,Wang Guoyin,Szczuka M. Rough Sets,Fuzzy Sets,Data Mining,and Granular Computing[A].Berlin:Springer-Verlag,2003.
  • 6陆汝钤.人工智能(上)[M]北京:科学出版社,2002.
  • 7赵继广,肖威,安娜,宋建军.基于粗糙集约简算法的加注系统风险预测模型[J].装备指挥技术学院学报,2011,22(1):116-119. 被引量:3
  • 8赵亚娣,魏立力.基于变精度粗糙集的不完备信息系统知识约简[J].计算机工程与应用,2009,45(13):65-67. 被引量:13

二级参考文献12

  • 1Pawlak Z.Rough sets[J].International Journal of Information and Computer Science,1982,11:341-356.
  • 2Kryszkiewiez M.Rules in incomplete information systems[J].Information sciences,1999,113:271-292.
  • 3Stefanowski J.Incomplete information tables and rough classification[J].Computational Intelligence,2001,17(3):73-81.
  • 4Kryszkiewicz M.Rough set approach to incomplete information systems[J].Information sciences,1998,112:39-49.
  • 5Ziarko W.Variable precision rough set model[J].Journal of Computer and System Science,1993,46:39-59.
  • 6Beynon M.Reducts within the variable precision rough set model:A further investigation[J].European Journal of Operational Research,2001,124:592-605.
  • 7An Aijun,Shan Ning.Discovering rules for water demand prediction:An enhance rough set approach[J].Applications of Artificial Intelligenxe,1996,9(6):645-653.
  • 8PAWLAK Z.Vagueness acid uncertainty:a rough set perspective[J].Computational Intelligence,1995,4(11):227-232.
  • 9CONNOR J T,MATINE R D,ATLAS L E.Recurrent neutral networks and robust time series prediction[J].IEEE Transactions on Neural Networks,1994,5(2):240-254.
  • 10BALDl P,HORNIK K.Neural networks for principal cornponent analysis:learning from examples without local minima[J].Neural Networks,1989,3(2):53-58.

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