计算机工程2025，Vol.51Issue(5)：188-195,8.DOI:10.19678/j.issn.1000-3428.0068887
基于加权局部密度的双超球支持向量机算法
Twin-Hypersphere Support Vector Machine Algorithm Based on Weighted Local Density
摘要
Abstract
The Twin-Hypersphere Support Vector Machine(THSVM)algorithm determines the distribution of samples using a pair of hyperspheres.It assigns an identical weight to each feature and ignores the density information of samples and the influence of different features on the classification of the samples.It is also sensitive to noise.To address these issues,this paper proposes a THSVM algorithm based on weighted local density(WLDTHSVM).First,the information gain is used to calculate the weight of each feature,and the calculated weights of all features are used to compute the Euclidean distances and kernel functions.This step reduces the impact of irrelevant or weakly relevant features on the similarity of samples.Second,the weighted local density function is defined based on the weighted feature Euclidean distances.The weighted density function not only considers the class information of the nearest neighbors of the sample,but also takes into account the influence of different features on the sample spacing.It combines the normalized weighted local density with the error term to enhance the anti-noise ability of the model.Finally,a weighted feature decision function is proposed to determine the category to which a test sample belongs.The usability and effectiveness of the proposed algorithm are assessed using UCI datasets and two artificial datasets.The experimental results show that,compared with algorithm such as the Support Vector Machine(SVM),Twin Support Vector Machines(TWSVM),THSVM,the WLDTHSVM algorithm has up to a 2.76 percentage points higher average accuracy on 11 UCI datasets,and it has a better classification performance on noisy datasets.关键词
支持向量机/局部密度/特征权重/信息增益/核函数Key words
Support Vector Machine(SVM)/local density/feature weighting/information gain/kernel function分类
计算机与自动化引用本文复制引用
王梦珍,张德生,张晓..基于加权局部密度的双超球支持向量机算法[J].计算机工程,2025,51(5):188-195,8.基金项目
国家自然科学基金面上项目(12171388). （12171388）
