As an effective method, feature selection can reduce computational complexity and improve classification performance. A number of criteria exist for feature selection using labeled data, unlabeled data and pairwise constraints, most of which are based on the Euclidean distance. In this paper, we propose a filter method for feature selection with pairwise constraints, aiming to jointly evaluate a feature subset based on metric learning. Two criteria are designed based on the well-known Kullback-Leibler divergence for measuring the difference between must-link constraints and cannot-link constraints that can indicate the feature subset discrimination based on Keep It Simple and Straightforward (KISS) metric learning and Cross-view Quadratic Discriminant Analysis (XQDA) metric learning. To address the challenging feature selection problem, we formulate a sequential search algorithm guided by indicators that are simplified from the proposed criteria. Furthermore, we conducted several experiments on sleep staging based on electroencephalogram (EEG) recordings from the Sleep-EDF Database Expanded. The experimental results demonstrate the effectiveness of the proposed method compared with nine representative feature selection methods. On the data set from healthy volunteers and the data set from volunteers that had mild difficulty falling asleep, the classification average accuracies achieve 97.66% and 93.57% by using the proposed method, respectively.

中文翻译：

---

使用 Plausibility Score 进行睡眠分期：一种基于度量学习的新特征选择方法

特征选择作为一种有效的方法，可以降低计算复杂度，提高分类性能。使用标记数据、未标记数据和成对约束的特征选择存在许多标准，其中大部分基于欧几里得距离。在本文中，我们提出了一种具有成对约束的特征选择过滤方法，旨在基于度量学习联合评估特征子集。基于众所周知的 Kullback-Leibler 散度设计了两个标准，用于测量必须链接约束和不能链接约束之间的差异，可以指示基于保持简单和直接（KISS）度量学习和交叉的特征子集区分。查看二次判别分析 (XQDA) 度量学习。为了解决具有挑战性的特征选择问题，我们制定了一个由指标指导的顺序搜索算法，这些指标是从建议的标准中简化出来的。此外，我们根据来自 Sleep-EDF Database Expanded 的脑电图 (EEG) 记录进行了多项睡眠分期实验。实验结果证明了与九种代表性特征选择方法相比所提出方法的有效性。在健康志愿者的数据集和轻度入睡困难志愿者的数据集上，使用该方法的分类平均准确率分别达到了 97.66% 和 93.57%。我们基于来自 Sleep-EDF Database Expanded 的脑电图 (EEG) 记录进行了多项睡眠分期实验。实验结果证明了与九种代表性特征选择方法相比所提出方法的有效性。在健康志愿者的数据集和轻度入睡困难志愿者的数据集上，使用该方法的分类平均准确率分别达到了 97.66% 和 93.57%。我们基于来自 Sleep-EDF Database Expanded 的脑电图 (EEG) 记录进行了多项睡眠分期实验。实验结果证明了与九种代表性特征选择方法相比所提出方法的有效性。在健康志愿者的数据集和轻度入睡困难志愿者的数据集上，使用该方法的分类平均准确率分别达到了 97.66% 和 93.57%。