论文标题
非线性支持向量机分类中一种新型的嵌入式最小嵌入式最大方法用于特征选择
A novel embedded min-max approach for feature selection in nonlinear support vector machine classification
论文作者
论文摘要
近年来，在几个机器学习领域（例如分类问题）中，功能选择已成为一个具有挑战性的问题。支持向量机（SVM）是一种应用于分类任务的众所周知的技术。文献中已经提出了各种方法，以选择SVM中最相关的特征。不幸的是，所有这些都可以处理线性分类设置中的特征选择问题，或者提出在实践中难以实施的临时方法。相比之下，我们提出了一种基于最低最大优化问题的嵌入式特征选择方法，其中寻求模型复杂性和分类精度之间的权衡。通过利用偶性理论，我们等效地重新重新制定了Min-Max问题，并在不使用现成的软件进行非线性优化的情况下解决了这一问题。我们的方法的效率和实用性在精确性，选定功能的数量和解释性方面对几个基准数据集进行了测试。
In recent years, feature selection has become a challenging problem in several machine learning fields, such as classification problems. Support Vector Machine (SVM) is a well-known technique applied in classification tasks. Various methodologies have been proposed in the literature to select the most relevant features in SVM. Unfortunately, all of them either deal with the feature selection problem in the linear classification setting or propose ad-hoc approaches that are difficult to implement in practice. In contrast, we propose an embedded feature selection method based on a min-max optimization problem, where a trade-off between model complexity and classification accuracy is sought. By leveraging duality theory, we equivalently reformulate the min-max problem and solve it without further ado using off-the-shelf software for nonlinear optimization. The efficiency and usefulness of our approach are tested on several benchmark data sets in terms of accuracy, number of selected features and interpretability.
