计算机科学 ›› 2022, Vol. 49 ›› Issue (4): 152-160.doi: 10.11896/jsjkx.210300094
孙林1,2, 黄苗苗1,3, 徐久成1,2
SUN Lin1,2, HUANG Miao-miao1,3, XU Jiu-cheng1,2
摘要： 在多标记学习与分类中,现有邻域粗糙集特征选择算法若将样本的分类间隔作为邻域半径,则会出现分类间隔过大导致分类无意义、样本距离过大容易造成异类样本和同类样本失效,以及无法处理弱标记数据等情况。为解决这些问题,提出一种基于多标记邻域粗糙集和多标记Relief的弱标记特征选择方法。首先,引入异类样本数和同类样本数来改进分类间隔,在此基础上定义邻域半径,构造新的邻域近似精度与多标记邻域粗糙集模型,并有效度量边界域引起的集合不确定性。其次,利用迭代更新权重公式填补大部分缺失标记信息,将邻域近似精度与互信息相结合,以构造新的标记相关性,填补剩余的缺失标记信息。然后,使用异类样本数和同类样本数,以构造新的标记权重和特征权重计算公式,进而提出多标记Relief模型,并将其应用于多标记特征选择。最后,结合多标记邻域粗糙集模型和多标记Relief算法,设计一种新的弱标记特征选择算法,以处理带有缺失标记的高维数据,并有效地提升多标记分类性能。在11个公共多标记数据集上进行仿真实验,结果验证了所提出的弱标记特征选择算法的有效性。
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