基于动态相关性的特征选择算法

特征选择是从原始数据集中去除无关的特征并选择良好的特征子集，可以避免维数灾难和提高学习算法的性能。为解决已选特征和类别动态变化（DCSF）算法在特征选择过程中只考虑已选特征和类别之间动态变化的信息量，而忽略候选特征和已选特征的交互相关性的问题，提出了一种基于动态相关性的特征选择（DRFS）算法。该算法采用条件互信息度量已选特征和类别的条件相关性，并采用交互信息度量候选特征和已选特征发挥的协同作用，从而选择相关特征并且去除冗余特征以获得优良特征子集。仿真实验表明，与现有算法相比，所提算法能有效地提升特征选择的分类准确率。

Dynamic relevance based feature selection algorithm

By removing irrelevant features from the original dataset and selecting good feature subsets， feature selection can avoid the curse of dimensionality and improve the performance of learning algorithm.In the process of feature selection， only the dynamically change information between the selected features and classes is considered， and interaction relevance between the candidate features and the selected features is ignored by Dynamic Change of Selected Feature with the class （DCSF） algorithm. To solve this problem， a Dynamic Relevance based Feature Selection （DRFS） algorithm was proposed. In the proposed algorithm， conditional mutual information was used to measure the conditional relevance between the selected features and classes， and interaction information was used to measure the synergy brought by the candidate features and the selected features， so as to select relevant features and remove redundant features then obtain good feature subsets. Simulation results show that， compared with existing algorithms， the proposed algorithm can effectively improve classification accuracy of feature selection.