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A Multiple Kernel Support Vector Machine Scheme for Simultaneous Feature Selection and Rule-Based Classification

机译:同时选择特征和基于规则的分类的多核支持向量机方案

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In many applications such as bioinformatics and medical decisionmaking, the interpretability is important to make the model acceptable to the user and help the expert discover the novel and perhaps valuable knowledge hidden behind the data. This paper presents a novel feature selection and rule extraction method which is based on multiple kernel support vector machine (MK-SVM). This method has two outstanding properties. Firstly, the multiple kernels are described as the convex combination of the single feature basic kernels. It makes the feature selection problem in the context of SVM transformed into an ordinary multiple parameters learning problem. A 1-norm based linear programming is proposed to carry out the optimization of those parameters. Secondly, the rules are obtained in an easy way: only the support vectors necessary. It is demonstrated in theory that every support vector obtained by this method is just the vertex of the hypercube. Then a tree-like algorithm is proposed to extract the if-then rules. Three UCI datasets are used to demonstrate the effectiveness and efficiency of this approach.
机译:在诸如生物信息学和医学决策等许多应用中,可解释性对于使模型被用户接受并帮助专家发现隐藏在数据背后的新颖且可能有价值的知识很重要。本文提出了一种基于多核支持向量机(MK-SVM)的特征选择和规则提取新方法。该方法具有两个突出的特性。首先,将多个核描述为单特征基本核的凸组合。它使支持向量机中的特征选择问题转化为普通的多参数学习问题。提出了一种基于1-范数的线性规划方法,以对这些参数进行优化。其次,以简单的方式获得规则:仅需要支持向量。从理论上证明,通过此方法获得的每个支持向量都只是超立方体的顶点。然后提出了一种树状算法来提取if-then规则。使用三个UCI数据集来证明此方法的有效性和效率。

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