摘要: 针对数据挖掘与模式识别领域中的高维数据处理问题,通过分析样本类间距离与类内距离,给出一种基于图理论的特征排序框架。根据该框架,提出使用类内-类间和K近邻相似度定义的2种快速特征选择算法,能避免复杂度较高的广义特征分解过程。实验结果表明,该算法具有较高的分类精度。
关键词:
数据挖掘,
模式识别,
特征选择,
图模型,
特征分解,
K近邻
Abstract: The high dimensionality of the data samples often makes the data mining or pattern recognition tasks intractable, through analyzing both the within-class distance and between-class distance, it presents a fast feature ranking framework, from which the computationally expensive feature decomposition is avoided. Two similarity measures of within-class and between-class similarity and K nearest neighbor similarity are employed to derive efficient feature selection algorithms. Experimental results demonstrate that these algorithms have higher classification precision.
Key words:
data mining,
pattern recognition,
feature selection,
graph model,
feature decomposition,
K nearest neighbor
中图分类号:
潘锋, 王建东, 顾其威, 牛奔. 基于图的特征选择算法[J]. 计算机工程, 2012, 38(9): 197-198,201.
BO Feng, WANG Jian-Dong, GU Ji-Wei, NIU Ben. Feature Selection Algorithm Based on Graph[J]. Computer Engineering, 2012, 38(9): 197-198,201.