支持向量机的一种特征选取算法

Feature selection algorithm in support vector machine

支持向量机(Support Vector Machine,SVM)是一种有效的分类方法,其学习本质是通过对偶问题求解原问题,但是它不能直接获得特征重要性。提出一种新的特征选取算法,实验表明,该特征选取算法与一般特征选取算法(如F-Score算法)相比,对同一测试数据集计算的结果具有相同的降序排列结果,而且有更好的特征刻画量化指标,分界线更明显,表明新的特征选取算法具有更佳的合理性。

SVM is an effective classification method,the nature of learning is that it solves the original problem by the dual problem,but it does not directly obtain the feature importance.This article raises a new feature selection algorithm.Experiments show that the new feature selection algorithm and F-Score algorithm for calculating the same test data set is the result of the same descending order.The new feature selection algorithm is reasonable.