Abstract: As a data preprocessing process, feature selection plays an important role in data mining, pattern recognition and machine learning. Through feature selection, the complexity of the problem can be reduced, and the prediction accuracy, robustness and interpretability of the learning algorithm can be improved. This paper introduces the framework of feature selection methods, and focuses on the two processes of generating feature subsets and evaluation criteria. The feature selection algorithms are classified according to different combinations of feature selection and learning algorithms, and the advantages and disadvantages of various methods are analyzed. The existing problems of existing feature selection algorithms are discussed, and some research difficulties and research directions are proposed.

Key words: feature selection, search strategy, evaluation criteria, feature selection classification

摘要： 特征选择作为一个数据预处理过程，在数据挖掘、模式识别和机器学习中有着重要地位。通过特征选择，可以降低问题的复杂度，提高学习算法的预测精度、鲁棒性和可解释性。介绍特征选择方法框架，重点描述生成特征子集、评价准则两个过程；根据特征选择和学习算法的不同结合方式对特征选择算法分类，并分析各种方法的优缺点；讨论现有特征选择算法存在的问题，提出一些研究难点和研究方向。

关键词: 特征选择, 搜索策略, 评价准则, 特征选择分类