Solving a class of feature selection problems via fractional 0–1 programming
Solving a class of feature selection problems via fractional 0–1 programming
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DOI：
10.1007/s10479-020-03917-w
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发表时间：
2021-03
影响因子：
4.8
通讯作者：
Erfan Mehmanchi;A. Gómez;O. Prokopyev
中科院分区：
文献类型：
--
作者：
Erfan Mehmanchi;A. Gómez;O. Prokopyev
特征选择是许多机器学习和模式识别系统的基本预处理步骤。值得注意的是，一些基于互信息和基于相关性的特征选择问题可以表示为具有单一比率的多项式 0-1 函数的分数程序。 在本文中，我们研究了确保这些特征选择问题的全局最优解决方案的方法。我们使用几个真实数据集进行计算实验并报告了令人鼓舞的结果。所考虑的解决方法对于中型和相当大型的数据集表现良好，而文献中现有的混合整数线性程序却失败了。
Feature selection is a fundamental preprocessing step for many machine learning and pattern recognition systems. Notably, some mutual-information-based and correlation-based feature selection problems can be formulated as fractional programs with a single ratio of polynomial 0–1 functions. In this paper, we study approaches that ensure globally optimal solutions for these feature selection problems. We conduct computational experiments with several real datasets and report encouraging results. The considered solution methods perform well for medium- and reasonably large-sized datasets, where the existing mixed-integer linear programs from the literature fail.