Algorithm for Visualization of Classification Results of Two-Category Data
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摘要: 针对在某些应用领域对二分类数据分类结果可视化的需求,以及现有无监督可视化算法无法提供分类结果的相关信息的问题,提出了二分类数据分类结果可视化算法———支持向量可视化(SVV).该算法是在无监督的自组织神经网络(SOM)的可视化功能的基础上,结合监督学习的支持向量机(SVM)的二分类算法,得到能够直观地显示高维数据、二分类数据分类边界以及数据与分类边界距离的二维映射图,提高了分类结果的可解释性.以SOM可视化算法以及Sammon算法为参照,用2组可分性不同的样本集进行仿真分析,验证了该算法的有效性和可行性.Abstract: A new algorithm called support vector visualization(SVV) was proposed for visualization of classification results of two-category data to meet the need in some applications.The SVV algorithm is based on support vector machine(SVM) and self-organizing mapping(SOM).The result of SVV is a 2D map to visualize highdimensional data,the boundary of the two-category data,as well as the distance between a datum and the boundary.Compared with SOM and Sammon mapping algorithms,experimental results on two datasets with different separability verify the feasibility and effectiveness of the SVV algorithm.
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Key words:
- SVM /
- SOM /
- visualization /
- algorithm /
- SVV /
- two-category data
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