Empirical study of feature selection methods based on individual feature evaluation for classification problems
Empirical study of feature selection methods based on individual feature evaluation for classification problems
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DOI：
10.1016/j.eswa.2010.12.160
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发表时间：
2011-07-01
影响因子：
8.5
通讯作者：
Benitez, Jose M.
中科院分区：
文献类型：
--
作者：
Arauzo-Azofra, Antonio;Aznarte, Jose Luis;Benitez, Jose M.
使用特征选择可以提高学习过程及其结果模型的准确性、效率、适用性和可理解性。为此，人们开发了许多自动特征选择的方法。通过使用特征选择过程的模块化，本文对这些方法进行了广泛的评估。所考虑的方法是通过结合不同的选择标准和单独的特征评估模块而创建的。这些方法因其运行时间短而被广泛使用。在进行彻底的实证研究后，确定了最有趣的方法，并提供了关于在不同条件下应使用哪种特征选择方法的一些建议。 (C) 2010 Elsevier Ltd. 保留所有权利。
The use of feature selection can improve accuracy, efficiency, applicability and understandability of a learning process and its resulting model. For this reason, many methods of automatic feature selection have been developed. By using a modularization of feature selection process, this paper evaluates a wide spectrum of these methods. The methods considered are created by combination of different selection criteria and individual feature evaluation modules. These methods are commonly used because of their low running time. After carrying out a thorough empirical study the most interesting methods are identified and some recommendations about which feature selection method should be used under different conditions are provided. (C) 2010 Elsevier Ltd. All rights reserved.