摘要
对于包含大量特征的数据集,特征选择已成为一个研究热点,能剔除无关和冗余特征,将会有效改善分类准确性.对此,在分析已有文献的基础上,提出一种基于属性关系的特征选择算法(NCMIPV),获取优化特征子集,并在UCI数据集上对NCMIPV算法进行性能评估.实验结果表明,与原始特征子集相比,该算法能有效降低特征空间维数,运行时间也相对较短,分类差错率可与其他算法相比,在某些场合下性能明显优于其他算法.
Feature selection has become a heated research issue for datasets that contain large numbers of features and has the ability to remove irrelevant and redundant features and improve classification accuracy in an effective fashion. A feature selection algorithm based on relationship between attributes named NCMIPV is proposed to acquire the optimized feature subset based on the analysis of existing relevant literatures, and the performance of NCMIPV on UCI datasets is evaluated.Experiment results show that compared with original datasets, this algorithm tends to shrink the dimension of feature space effectively in a comparatively shorter length of time. Moreover, the misclassification rate appears to rival other algorithms.Overall performance of the proposed algorithm is obviously superior to its counterparts in certain situation.
出处
《控制与决策》
EI
CSCD
北大核心
2015年第10期1903-1906,共4页
Control and Decision
关键词
特征选择
属性关系
分类
feature selection
attribute relationship
classification
