计算机科学 ›› 2016, Vol. 43 ›› Issue (4): 264-269.doi: 10.11896/j.issn.1002-137X.2016.04.054
梁路,黎剑,霍颖翔,滕少华
LIANG Lu, LI Jian, HUO Ying-xiang and TENG Shao-hua
摘要： 传统的数据标准化处理通常采用的是线性的变换方法,其在处理非均匀分布的数据集时,容易因局部区间内数据点间距过小导致后续的数据挖掘(尤其是基于距离的挖掘)结果不够精确。因此,为非均匀分布数据提出一种基于数据拟合的非线性变换标准化方法,该方法能够在不改变数据整体分布规律的前提下,依据统计找出对应的非线性变换函数,根据函数对各数据点的取值进行非线性放缩,将数据稠密的区间进行扩大的同时将数据稀疏的区间进行压缩,让挖掘的结果更加精确。实验采用BP(Back Propagation)神经网络、支持向量机(Support Vector Machine,SVM)、最近邻分类(K-Nearest Neighbor,KNN) 3种经典分类算法结合不同的数据集进行了挖掘,结果表明,分类的错误率有不同程度的下降,同时F1度量有所提高。
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