Abstract:

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.

Key words: feature selection, online streaming feature selection, hierarchical classification, ReliefF algorithm, sibling strategy

摘要：

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。首先将类别之间的层次关系融入ReliefF算法中，定义一种新的面向层次化数据的特征权重计算算法HF_ReliefF；其次，利用特征对决策属性的划分能力动态选择重要特征；最后，基于特征之间的独立性对特征进行动态冗余分析。实验结果表明，与五种先进的在线流特征选择算法作对比，OH_ReliefF算法在K最邻近（KNN）分类器和拉格朗日支持向量机（LSVM）分类器的各个评价指标中都取得较优的结果，准确率最少提高7个百分点。

关键词: 特征选择, 在线流特征选择, 层次分类, ReliefF算法, 兄弟策略