基于约束投影的支持向量机选择性集成

提出两种基于约束投影的支持向量机选择性集成算法。首先利用随机选取的must-link和cannot-link成对约束集确定投影矩阵,将原始训练样本投影到不同的低维空间训练一组基分类器;然后,分别采用遗传优化和最小化偏离度误差两种选择性集成技术对基分类器进行组合。基于UCI数据的实验表明,提出的两种集成算法均能有效提高支持向量机的泛化性能,显著优于Bagging,Boosting,特征Bagging及LoBag等集成算法。

Constraint Projection-based Support Vector Machines Selective Ensemble Algorithms

This paper proposed two constraint project-based selective ensemble algorithms of support vector machines.Firstly,projective matrices were determined upon randomly selected must-link and cannot-link constraint sets,with which original training samples were transformed into different representation spaces to learn a group of base classifiers.Then,two selective ensemble techniques of genetic optimization and minimizing deviation errors were utilized to combine base classifiers.Experiments on UCI datasets show...