Feature selection has emerged as a craft, using which we boost the performance of our learning model. Feature or Attribute Selection is a data preprocessing technique, where only the most informative features are considered and given to the predictor. This reduces the computational overhead and improves the correctness of the classifier. Attribute Selection is commonly carried out by applying some filter or by using the performance of the learning model to gauge the quality of the attribute subset. Metric Ranked Feature Inclusion and Accuracy Ranked Feature Inclusion are the two novel hybrid feature selection methods we introduce in this paper. These algorithms follow a two-stage procedure, the first of which is feature ranking, followed by feature subset selection. They differ in the way they rank the features but follow the same subset selection technique. Multiple experiments have been conducted to assess our models. We compare our results with numerous works of the past and validate our models using 12 datasets. From the results, we infer that our algorithms perform better than many existent models.

中文翻译：

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度量和准确度排序特征包含：过滤器和包装器特征选择方法的混合

特征选择已经成为一种工艺，使用它我们可以提高我们的学习模型的性能。特征或属性选择是一种数据预处理技术，其中仅考虑信息量最大的特征并将其提供给预测器。这减少了计算开销并提高了分类器的正确性。属性选择通常通过应用一些过滤器或通过使用学习模型的性能来衡量属性子集的质量来进行。Metric Ranked Feature Inclusion 和 Accuracy Ranked Feature Inclusion 是我们在本文中介绍的两种新颖的混合特征选择方法。这些算法遵循两个阶段的过程，首先是特征排序，然后是特征子集选择。它们在对特征进行排序的方式上有所不同，但遵循相同的子集选择技术。已经进行了多次实验来评估我们的模型。我们将我们的结果与过去的大量工作进行比较，并使用 12 个数据集验证我们的模型。从结果中，我们推断我们的算法比许多现有模型表现得更好。