Feature selection algorithms are the cornerstone of machine learning. By increasing the properties of the samples and samples, the feature selection algorithm selects the significant features. The general name of the methods that perform this function is the feature selection algorithm. The general purpose of feature selection algorithms is to select the most relevant properties of data classes and to increase the classification performance. Thus, we can select features based on their classification performance. In this study, we have developed a feature selection algorithm based on decision support vectors classification performance. The method can work according to two different selection criteria. We tested the classification performances of the features selected with P-Score with three different classifiers. Besides, we assessed P-Score performance with 13 feature selection algorithms in the literature. According to the results of the study, the P-Score feature selection algorithm has been determined as a method which can be used in the field of machine learning.

特征选择算法是机器学习的基石。通过增加样本和样本的属性，特征选择算法可以选择重要特征。执行此功能的方法的总称是特征选择算法。特征选择算法的一般目的是选择数据类的最相关属性并提高分类性能。因此，我们可以根据特征的分类性能进行选择。在这项研究中，我们开发了一种基于决策支持向量分类性能的特征选择算法。该方法可以根据两个不同的选择标准来工作。我们使用三种不同的分类器测试了使用P-Score选择的功能的分类性能。除了，我们在文献中使用13种特征选择算法评估了P-Score性能。根据研究结果，P-Score特征选择算法已确定为可用于机器学习领域的方法。