CAJ | 학술논문

粒度支持向量机( GSVM)在处理分布均匀的数据集时较有效,但现实生活中数据集的分布往往是不可预测的,且分布不均匀。文中提出一种动态粒度支持向量机( DGSVM)学习算法,根据粒的不同分布自动粒划分,使SVM可在不同层次的粒上训练。标准数据集上的实验表明,与GSVM相比,DGSVM具有更好的分类性能。
립도지지향량궤( GSVM)재처리분포균균적수거집시교유효,단현실생활중수거집적분포왕왕시불가예측적,차분포불균균。문중제출일충동태립도지지향량궤( DGSVM)학습산법,근거립적불동분포자동립화분,사SVM가재불동층차적립상훈련。표준수거집상적실험표명,여GSVM상비,DGSVM구유경호적분류성능。
Granular support vector machine ( GSVM ) is effective when dealing with distribution uniform datasets. However, the distribution of the dataset in the real world is unpredictable, and the density is uneven. In this paper, a dynamic granular support vector machine learning algorithm ( DGSVM ) is proposed. According to the different distribution of the granules, some granules are divided automatically and SVM training is performed on different levels of granule space. The experimental results on benchmark datasets demonstrate that DGSVM algorithm obtains better classification performance compared with GSVM.