Spectral analysis and Information-theory are two powerful and successful frameworks for feature selection in supervised classification problems. However, most of the methods developed under these frameworks have been introduced for handling exclusively numerical or non- numerical data. In this paper, we propose a supervised filter feature selection method that combines Spectral Feature Selection and Information-theory based redundancy analysis for selecting relevant and non-redundant features in supervised mixed datasets; i.e., datasets where the objects are described simultaneously by both, numerical and non-numerical features. To demonstrate the effectiveness of our proposed supervised filter feature selection method, we conducted several experiments on 40 public real-world datasets. Additionally, we compare our method against relevant state-of-the-art supervised filter methods for numerical, non-numerical, and mixed data. From this comparison, our method, in general, obtains better results than the results obtained by the other evaluated filter feature selection methods.

光谱分析和信息理论是有监督的分类问题中功能选择的两个强大而成功的框架。但是，已经引入了在这些框架下开发的大多数方法，专门用于处理数字或非数字数据。在本文中，我们提出了一种监督过滤器特征选择方法，该方法将频谱特征选择和基于信息论的冗余分析相结合，以选择监督混合数据集中的相关和非冗余特征。即，通过数值和非数值特征同时描述对象的数据集。为了证明我们提出的监督过滤器特征选择方法的有效性，我们对40个公共现实世界数据集进行了几次实验。另外，我们将我们的方法与相关的最新监督过滤方法进行了比较，以处理数值，非数值和混合数据。通过这种比较，我们的方法通常比其他评估的过滤器特征选择方法获得更好的结果。