In this paper, a new feature selection algorithm, called SFE (Simple, Fast, and Efficient), is proposed for high-dimensional datasets. The SFE algorithm performs its search process using a search agent and two operators: non-selection and selection. It comprises two phases: exploration and exploitation. In the exploration phase, the non-selection operator performs a global search in the entire problem search space for the irrelevant, redundant, trivial, and noisy features, and changes the status of the features from selected mode to non-selected mode. In the exploitation phase, the selection operator searches the problem search space for the features with a high impact on the classification results, and changes the status of the features from non-selected mode to selected mode. The proposed SFE is successful in feature selection from high-dimensional datasets. However, after reducing the dimensionality of a dataset, its performance cannot be increased significantly. In these situations, an evolutionary computational method could be used to find a more efficient subset of features in the new and reduced search space. To overcome this issue, this paper proposes a hybrid algorithm, SFE-PSO (particle swarm optimization) to find an optimal feature subset. The efficiency and effectiveness of the SFE and the SFE-PSO for feature selection are compared on 40 high-dimensional datasets. Their performances were compared with six recently proposed feature selection algorithms. The results obtained indicate that the two proposed algorithms significantly outperform the other algorithms, and can be used as efficient and effective algorithms in selecting features from high-dimensional datasets.

翻译：本文提出了一种名为SFE（简单、快速且高效）的新型特征选择算法，专门用于高维数据集。SFE算法通过一个搜索代理以及两个算子——非选择算子与选择算子——执行搜索过程。该算法包含两个阶段：探索阶段与利用阶段。在探索阶段，非选择算子在整个问题搜索空间中对不相关、冗余、琐碎及噪声特征进行全局搜索，并将特征状态从已选模式转变为未选模式。在利用阶段，选择算子则搜索问题空间中那些对分类结果具有高影响力的特征，并将特征状态从未选模式转变为已选模式。所提出的SFE算法在高维数据集的特征选择中表现出色。然而，在降低数据集维度后，其性能无法显著提升。在此类情形下，可采用进化计算方法在缩减后的新搜索空间中寻找更优的特征子集。为解决此问题，本文提出了一种混合算法SFE-PSO（粒子群优化），以寻找最优特征子集。通过40个高维数据集，对比评估了SFE算法与SFE-PSO算法在特征选择中的效率与有效性，并与六种近期提出的特征选择算法进行了性能比较。结果表明，所提出的两种算法显著优于其他对比算法，可作为高维数据集特征选择的高效且有效的算法。