This paper introduces a novel graph-based filter method for automatic feature
selection (abbreviated as GB-AFS) for multi-class classification tasks. The
method determines the minimum combination of features required to sustain
prediction performance while maintaining complementary discriminating abilities
between different classes. It does not require any user-defined parameters such
as the number of features to select. The methodology employs the
Jeffries-Matusita (JM) distance in conjunction with t-distributed Stochastic
Neighbor Embedding (t-SNE) to generate a low-dimensional space reflecting how
effectively each feature can differentiate between each pair of classes. The
minimum number of features is selected using our newly developed Mean
Simplified Silhouette (abbreviated as MSS) index, designed to evaluate the
clustering results for the feature selection task. Experimental results on
public data sets demonstrate the superior performance of the proposed GB-AFS
over other filter-based techniques and automatic feature selection approaches.
Moreover, the proposed algorithm maintained the accuracy achieved when
utilizing all features, while using only $7\%$ to $30\%$ of the features.
Consequently, this resulted in a reduction of the time needed for
classifications, from $15\%$ to $70\%$.

该论文介绍了一种用于多类别分类的自动特征选择的新型基于图的滤波方法（缩写为 GB-AFS），它确定了在维持预测性能的同时保持不同类别之间互补判别能力的最小特征组合。该方法不需要任何用户定义的参数，如要选择的特征数量。该方法使用 Jeffries-Matusita (JM) 距离结合 t 分布随机邻域嵌入（t-SNE）生成反映每个特征能够在每对类别之间区分性的低维空间。采用我们新开发的用于特征选择任务的平均简化 Silhouette (MSS) 指数选择最小特征数量，该指数旨在评估特征选择任务的聚类结果。在公开数据集上的实验结果表明，所提出的 GB-AFS 方法优于其他基于滤波的技术和自动特征选择方法的性能。此外，所提出的算法在仅使用特征的 7% 到 30% 的情况下保持了达到的准确性，从而将分类所需的时间减少了 15% 到 70%。