【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。更多还原

【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.更多还原