提出CF-WFCM算法,该算法分为属性权重学习算法和聚类算法两部分.属性权重学习算法,从数据自身的相似性出发,通过梯度递减算法极小化属性评价函数CFuzziness(w),为每个属性赋予一个权重.将属性权重应用于Fuzzy C Mean聚类算法,得到CF-WFC...提出CF-WFCM算法,该算法分为属性权重学习算法和聚类算法两部分.属性权重学习算法,从数据自身的相似性出发,通过梯度递减算法极小化属性评价函数CFuzziness(w),为每个属性赋予一个权重.将属性权重应用于Fuzzy C Mean聚类算法,得到CF-WFCM算法的聚类算法.CF-WFCM算法强化重要属性在聚类过程中的作用,消减冗余属性的作用,从而改善聚类的效果.我们选取了部分UCI数据库进行实验,实验结果证明:CF-WFCM算法的聚类结果优于FCM算法的聚类结果.函数CFuzziness(w)不仅可以评价属性的重要性,而且可以评价属性评价函数的优劣.实验说明了这一问题.最后我们对CF-WFCM算法进行了讨论.展开更多
Dempster-Shafer evidence theory(DS theory) is widely used in brain magnetic resonance imaging(MRI) segmentation,due to its efficient combination of the evidence from different sources. In this paper, an improved MRI s...Dempster-Shafer evidence theory(DS theory) is widely used in brain magnetic resonance imaging(MRI) segmentation,due to its efficient combination of the evidence from different sources. In this paper, an improved MRI segmentation method,which is based on fuzzy c-means(FCM) and DS theory, is proposed. Firstly, the average fusion method is used to reduce the uncertainty and the conflict information in the pictures. Then, the neighborhood information and the different influences of spatial location of neighborhood pixels are taken into consideration to handle the spatial information. Finally, the segmentation and the sensor data fusion are achieved by using the DS theory. The simulated images and the MRI images illustrate that our proposed method is more effective in image segmentation.展开更多
文摘提出CF-WFCM算法,该算法分为属性权重学习算法和聚类算法两部分.属性权重学习算法,从数据自身的相似性出发,通过梯度递减算法极小化属性评价函数CFuzziness(w),为每个属性赋予一个权重.将属性权重应用于Fuzzy C Mean聚类算法,得到CF-WFCM算法的聚类算法.CF-WFCM算法强化重要属性在聚类过程中的作用,消减冗余属性的作用,从而改善聚类的效果.我们选取了部分UCI数据库进行实验,实验结果证明:CF-WFCM算法的聚类结果优于FCM算法的聚类结果.函数CFuzziness(w)不仅可以评价属性的重要性,而且可以评价属性评价函数的优劣.实验说明了这一问题.最后我们对CF-WFCM算法进行了讨论.
基金supported by the National Natural Science Foundation of China(6167138461703338)+2 种基金the Natural Science Basic Research Plan in Shaanxi Province of China(2016JM6018)the Project of Science and Technology Foundationthe Fundamental Research Funds for the Central Universities(3102017OQD020)
文摘Dempster-Shafer evidence theory(DS theory) is widely used in brain magnetic resonance imaging(MRI) segmentation,due to its efficient combination of the evidence from different sources. In this paper, an improved MRI segmentation method,which is based on fuzzy c-means(FCM) and DS theory, is proposed. Firstly, the average fusion method is used to reduce the uncertainty and the conflict information in the pictures. Then, the neighborhood information and the different influences of spatial location of neighborhood pixels are taken into consideration to handle the spatial information. Finally, the segmentation and the sensor data fusion are achieved by using the DS theory. The simulated images and the MRI images illustrate that our proposed method is more effective in image segmentation.
