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基于空间覆盖的半监督特征选择方法 被引量：2

Spatial overlapping based semi-supervised feature selection
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摘要 提出一种新颖的基于空间覆盖的半监督特征选择方法。该算法同时利用已标签数据与未标签数据进行特征选择,各特征的相关性大小由其在不同簇中的覆盖程度衡量。在公共数据集和毒性数据集上的实验表明,该方法在改善学习精度上有很好的应用前景。 A novel Spatial Overlapping based Semi-supervised Feature Selection （SOS-FS） method is proposed.It uses both labeled and unlabeled data in feature selection,feature＇s relevance is measured by its overlapping ratio among different clusters. Experimental results carried out on some public datasets collected from the UCI machine learning repository and predictive toxicology domain show that SOS-FS has a promising performance on the improvement of the learning accuracy.
作者 陈红 郭躬德
机构地区 福建师范大学数学与计算机科学学院 福建师范大学网络安全与密码技术重点实验室
出处 《计算机工程与应用》 CSCD 北大核心 2010年第8期130-132,140,共4页 Computer Engineering and Applications
基金 福建省自然科学基金 Grant No.2007J0016~~
关键词 空间覆盖 半监督 特征选择 spatial overlapping semi-supervised feature selection
分类号 TP311 [自动化与计算机技术—计算机软件与理论]
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参考文献11

  • 1Handl J,Knowles J.Feature subset selection in unsupervised learning via muhiobjective optimization[J].Int'l J Computational Intelligence Research,2006,2(3) :217-238.
  • 2Dash M,Liu H.Handling large unsupervised data via dimensionality reduction[C]//Proceedings of 1999 SIGMOD Research Issues in Data Mining and Knowledge Discovery(DMKD-99) Workshop, 1999.
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同被引文献26

  • 1张丽新,王家钦,赵雁南,杨泽红.机器学习中的特征选择[J].计算机科学,2004,31(11):180-184. 被引量：18
  • 2Lan H Witten, Eibe Frank. Data mining practical machine learning ools and techniques[ M]. 2nd Edition, San Francisco: Morgan Kaufmann, 2005.
  • 3UCI repository of machine learning databases [ EB/OL]. http:// www. ics. uci. eu/-mlearn/MLRository. html. 2008.
  • 4ndl J,Knowles J. Feature subset selection in unsupervised learning via multiobjective optimization [ J ]. Int'l J. Computational Intelligence Research, 2006,2(3) :21%238.
  • 5Dash M, Liu H. Handling large unsupervised data via dimensionality reduction[C]. In: Proceedings of 1999 SIGMOD Research Issues in Data Mining and Knowledge Discovery. DMKD-99, Work-shop, 1999.
  • 6Yvan Saeys, Thomas Abeel,Yves Van de Peer. Robust feature selection using ensemble feature selection techniques[M]. Machine Learning and Knowledge Discovery in Databases, vol 5212. Springer Berlin / Heidelberg, 2008,313-325.
  • 7Hand D, Marmila H, Smyth P. Principles of data mining [ M ]. The MIT Press, 2001.
  • 8Pierre-Emmanuel Jouve, Nicolas Nicoloyannis. A filter feature selection method for clustering[ M]. Foundations of Intelligent Systerns,vol 3488. Springer Berlin / Heidelberg, 2005,583-593.
  • 9Ren Jiang-tao, Qiu Zheng-yuan, Wei Fan,et al. Forward semi-supervised feature selection[ M]. Advances in Knowledge Discovery and Data Mining, voi 5012. Springer Berlin / Heidelberg, 2008, 970-976.
  • 10Marina Skuriehina, Robert P W Duin. Combining feature subsets in feature selection [ M]. Multiple Classifier Systems, vol 3541. Springer Berlin / Heidelberg, 2005,165-175.

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计算机工程与应用
2010年 第8期

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