基于熵值法的加权最小二乘支持向量机

Weighted Least Squares Support Vector Machine Based on Entropy Evaluation

支持向量机是一种以统计学习理论为基础的机器学习算法,着重解决小样本的建模问题,并且对非线性高维数据具有较好的处理能力。通常对于多维特征的数据,会对每一维数据做归一化处理以消除量纲的影响,但缺点在于忽视了各维特征的权重差异。提出了一种加权最小二乘支持向量机的建模方法,通过熵值法确定每一维特征的权重,根据特征权重对数据进行加权处理,最后由最小二乘支持向量机建立该系统模型。实验表明,对于多维特征的数据,所提方法具有更好的建模效果。

Support vector machine is a kind of machine learning algorithm based on statistical learning theory, which has a desirable modeling performance for nonlinear and high-dimensional data,even in the case of small samples. Typically, the data with multiple features would be normalized due to different dimensions. However, it ignores the dissimilarity of different features. A weighted least squares support vector machine was proposed. According to the entropy evaluation method, the feature weights may be determined so that the data could be normalized and weighted. Then the system model would be established through the least squares support vector machine. The experimental results demonstrate the effectiveness and superiority of the proposed method for the system with multiple features.