基于ReliefF的层次分类在线流特征选择算法

在图像标注、疾病诊断等实际分类任务中,数据标记空间的类别通常存在着层次化结构关系,且伴随着特征的高维性.许多层次特征选择算法因不同的实际任务需求而提出,但这些已有的特征选择算法忽略了特征空间的未知性和不确定性.针对上述问题,提出一种基于ReliefF的面向层次分类学习的在线流特征选择算法OH_ReliefF.首先将类别...

Hierarchical classification online streaming feature selection algorithm based on ReliefF algorithm

In practical classification tasks such as image annotation and disease diagnosis， there is usually a hierarchical structural relationship between the classes in the label space of data with high dimensionality of the features. Many hierarchical feature selection algorithms have been proposed for different practical tasks， but ignoring the unknown and uncertainty of feature space. In order to solve the above problems， an online streaming feature selection algorithm OH_ReliefF based on ReliefF for hierarchical classification learning was presented. Firstly， the hierarchical relationship between classes was incorporated into the ReliefF algorithm to define a new method HF_ReliefF for calculating feature weights for hierarchical data. Then， important features were dynamically selected based on the ability of features to classify decision attributes. Finally， the dynamic redundancy analysis of features was performed based on the independence between features. Experimental results show that the proposed algorithm achieves better results in all evaluation metrics of the K-Nearest Neighbor （KNN） classifier and the Lagrangian Support Vector Machine （LSVM） classifier at least 7 percentage points improvement in accuracy when compared with five advanced online streaming feature selection algorithms.