CAJ | 학술논문

为改进面向不平衡数据的SVM分类器性能,以结构化SVM为基础,提出一种基于代价敏感的结构化支持向量机集成分类器模型.该模型首先通过训练样本的聚类,得到隐含在数据中的结构信息,并对样本进行初始加权.运用AdaBoost策略对各样本的权重进行动态调整,适当增大少数类样本的权重,使小类中误分的样本代价增大,以此来改进不平衡数据的分类性能.实验结果表明,该算法可有效提高不平衡数据的分类性能.
위개진면향불평형수거적SVM분류기성능,이결구화SVM위기출,제출일충기우대개민감적결구화지지향량궤집성분류기모형.해모형수선통과훈련양본적취류,득도은함재수거중적결구신식,병대양본진행초시가권.운용AdaBoost책략대각양본적권중진행동태조정,괄당증대소수류양본적권중,사소류중오분적양본대개증대,이차래개진불평형수거적분류성능.실험결과표명,해산법가유효제고불평형수거적분류성능.
@@@@To improve the performance of Support Vector Machine(SVM) classifier for imbalanced data, an ensemble classifier model based on structural SVM is introduced by incorporating cost-sensitive strategy. In the proposed classifier model, the training data is partitioned into several group by Ward hierarchical clustering algorithm, the structure information hidden in data is obtained, and the weight of every sample is initialized by using the prior knowledge hidden in clusters. Furthermore, employing AdaBoost strategy, the weight of each sample is dynamically adjusted effectively, and the weights of minority class samples are relatively increased. Hence, the cost of the misclassified positive samples is also increased for improving the classification accuracy of positive samples(minority class samples). The experimental results show that the proposed model effectively improves the classification performance of the imbalanced data.