融合序列后向选择与支持向量机的混合式特征选择算法

维度灾难是机器学习任务中的常见问题，特征选择算法能够从原始数据集中选取出最优特征子集，降低特征维度.提出一种混合式特征选择算法，首先用卡方检验和过滤式方法选择重要特征子集并进行标准化缩放，再用序列后向选择算法（SBS）与支持向量机（SVM）包裹的SBS-SVM算法选择最优特征子集，实现分类性能最大化并有效降低特征数量.实验中，将包裹阶段的SBS-SVM与其他两种算法在3个经典数据集上进行测试，结果表明，SBS-SVM算法在分类性能和泛化能力方面均具有较好的表现.

Hybrid Feature Selection Algorithm for Fusion Sequence Backward Selection and Support Vector Machine

Dimensional disaster is a common problem in machine learning tasks. The feature selection algorithm can select the optimal feature subset from the original data set and reduce the feature dimension. A hybrid feature selection algorithm is proposed. Firstly, the chi-square test and filtering method are used to select the important feature subsets and normalize scale, and then SBS-SVM wrapped by SBS and SVM. The algorithm selects the optimal feature subset to maximize the classification performance and effectively reduce the number of features. In the experiment, the SBS-SVM in the parcel stage and the other two algorithms are tested on three classical data sets. The results show that the SBS-SVM algorithm has better performance in classification performance and generalization ability.