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: 

• TP18

• TP301.6