Unsupervised Double local weighting for feature selection

摘要：

In this paper we proposed a new method Double local weighting based in self organized map (som), features weighting and on two learning methods localobservation-Som and local-distance-Som. This method allows us to weight the observation and the distance simultaneously and avoid the user to choose the confidence criteria for the weighted approach observation or distance during the learning process. We illustrate the performance of the proposed method using different data, showing a better performance for new algorithm. We can also show that through deferent means of visualization, DIS-SOM, OBS-SOM, and Dlw-SOM algorithms provide various pieces of information that could.be used in practical applications.

关键词： Self Organizing Map unsupervised learning local weighting observation local weighting distance double local weighting

作者: Nadia Mesghouni Moncef Temanni

作者单位: LI3 Laboratory, ISG Tunis, University of Tunis, 92 Avenue 9 Avril 1983,1007 TUNIS, Tunisia Tunis, Tunisia

会议类型: 国际会议

会议名称: 2011 6th Joint International Information Technology and Artificial Intelligence Conference(2011年第六届IEEE联合国际信息技术与人工智能会议 IEEE ITAIC 2011)

会议地点: 重庆

会议语种:英文

页码: 413-417

在线出版日期: 2011-08-20（万方平台首次上网日期，不代表论文的发表时间）