OUTLINE Introduction Fuzzy cMeans Algorithm FCM Feature Selected
- Slides: 41
OUTLINE Introduction Fuzzy c-Means Algorithm (FCM) Feature Selected FCM Algorithm Sample Selected FCM Algorithm Selective Ensemble Concluding Remarks References
OUTLINE Introduction Fuzzy c-Means Algorithm (FCM) Feature Selected FCM Algorithm Sample Selected FCM Algorithm Selective Ensemble Concluding Remarks References
数据集的C划分 Crisp c-Partition Fuzzy c-Partition
OUTLINE Introduction Fuzzy c-Means Algorithm (FCM) Feature Selected FCM Algorithm Sample Selected FCM Algorithm Selective Ensemble Concluding Remarks References
OUTLINE Introduction Fuzzy c-Means Algorithm (FCM) Feature Selected FCM Algorithm Sample Selected FCM Algorithm Selective Ensemble Concluding Remarks References
特征选择性FCM算法 FCM目标函数: 基于特征加权的 FCM目标函数: Jie Li, Xinbo Gao, Licheng Jiao, “A novel feature weighted fuzzy clustering algorithm”, D. Slezak et al. (Eds. ): RSFDGr. C 2005, LNAI 3641, pp. 412 -420, 2005, Springer-Verlag Berlin Heidelberg 2005
Relief. F算法 Ø Relief算法是Kira和Rendell在 1992年提出的,限于解 决两类的分类问题的特征选择; Ø 1994年Kononenko扩展了Relief算法,使得Relief. F可 以解决多类问题的特征选择; Ø Relief. F算法是给特征集中每一特征赋予一定的权重。 Kira K. , Rendell L A. , A practical approach to feature selection, Proceedings of the 9 th International Workshop on Machine Leaning, San Francisco, CA: Morgan Kaufmann, 1992, 249 -256
实验结果 IRIS数据由四维空间中的150个样本点组成,每一个样本的4个分量分别表示 IRIS的Petal Length,Petal Width,Sepal Length和Sepal Width。 包含了3个IRIS种类Setosa,Versicolor和Virginica,每类各有50个样本。 其中Setosa与其它两类间较好地分离,而Versicolor和Virginica之间存在交迭。
W-k-Means Algorithm Joshua Zhexue Huang,Michael K. Ng, Hongqiang Rong, and Zichen Li, Automated Variable Weighting in k-Means Type Clustering, IEEE Trans. on PAMI, 27(5): 657 -668, 2005
W-k-Means Algorithm Joshua Zhexue Huang,Michael K. Ng, Hongqiang Rong, and Zichen Li, Automated Variable Weighting in k-Means Type Clustering, IEEE Trans. on PAMI, 27(5): 657 -668, 2005
OUTLINE Introduction Fuzzy c-Means Algorithm (FCM) Feature Selected FCM Algorithm Sample Selected FCM Algorithm Selective Ensemble Concluding Remarks References
Sample selective FCM 目标函数: 迭 代 公 式
FCM for Large Data Set 原子聚类 典型样本 样本加权 Jie Li, Xinbo Gao, Licheng Jiao, “A Novel Typical-Sample-Weighted Clustering Algorithm for Large Data Sets ”, Lecture Notes in Artificial Intelligence, LNAI 3801: 696 -703, 2005
FCM for Large Data Set 对聚类中 心的影响 算法的可 扩展性 Jie Li, Xinbo Gao, Licheng Jiao, “A Novel Typical-Sample-Weighted Clustering Algorithm for Large Data Sets ”, Lecture Notes in Artificial Intelligence, LNAI 3801: 696 -703, 2005
OUTLINE Introduction Fuzzy c-Means Algorithm (FCM) Feature Selected FCM Algorithm Sample Selected FCM Algorithm Selective Ensemble Concluding Remarks References
OUTLINE Introduction Fuzzy c-Means Algorithm (FCM) Feature Selected FCM Algorithm Sample Selected FCM Algorithm Selective Ensemble Concluding Remarks References
OUTLINE Introduction Fuzzy c-Means Algorithm (FCM) Feature Selected FCM Algorithm Sample Selected FCM Algorithm Selective Ensemble Concluding Remarks References
References 1. Jie Li, Xinbo Gao, Licheng Jiao, A novel feature weighted fuzzy clustering algorithm, D. Slezak et al. (Eds. ): RSFDGr. C 2005, LNAI 3641, pp. 412 -420, 2005, Springer-Verlag Berlin Heidelberg 2005 2. Kira K. , Rendell L A. , A practical approach to feature selection, Proceedings of the 9 th International Workshop on Machine Leaning, San Francisco, CA: Morgan Kaufmann, 1992, 249 -256 3. Joshua Zhexue. Huang,Michael K. Ng, Hongqiang Rong, and Zichen Li, Automated Variable Weighting in k-Means Type Clustering, IEEE Trans. on PAMI, 27(5): 657 -668, 2005 4. 高新波,李�, “基于加� FCM与����指�的多���像自�分割算法 ”,� 子学� ,32(4): 661 -664, 2004 5. Jie Li, Xinbo Gao, Licheng Jiao, A Novel Typical-Sample-Weighted Clustering Algorithm for Large Data Sets, CIS 2005 6. Breiman L. Bagging predicators. Machine Learning, 1996, 24(2): 123− 140. 7. 唐�,周志�,基于 Bagging的��性聚�集成,�件学�, 16(4): 496 -502, 2005
THANK YOU Xinbo Gao School of Electronic Engineering Xidian University Xi’an 710071 P. R. China xbgao@IEEE. org http: //see. xidian. edu. cn/faculty/xbgao Xidian University, Xi’an, China © 2006
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