基于特征间距的二次规划特征选取算法

Feature Selection Algorithm Based on Quadratic Programming with Margin Between features

提出一种基于特征间距的二次规划特征选取算法。首先,将特征在类内样本间和异类样本间的距离分别作为二次规划算法目标函数的二次项和一次项参数,用以搜索类内紧密、内间分离的分类特征;同时,通过对二次项和一次项的归一化来均衡特征在同类样本和异类样本之间的关系;然后,将二次规划算法优化后的最优解向量作为衡量特征对分类贡献的权重向量,再根据特征权重高低选取分类特征。特征选取方法在6个数据集中的特征选取实验结果表明了该方法的可行性和有效性。

A feature selection algorithm using quadratic programming is proposed based on feature margins.Firstly,the inner-class distance of features is taken as the coefficient of the quadratic terms in the objective function and the inter-class distance of features is used as the coefficient of the linear terms for searching informative features.The elements of the quadratic terms and the linear terms are normalized to balance the feature relation between inner class and inter-class.Then,the optimal solution vector is taken as the feature weight vector for selecting informative features.Finally,experiments on six different datasets show the effectiveness and feasibility of the proposed method.