计算机科学 ›› 2015, Vol. 42 ›› Issue (5): 106-108.doi: 10.11896/j.issn.1002-137X.2015.05.021
徐晓丹,姚明海,刘华文,郑忠龙
XU Xiao-dan, YAO Ming-hai, LIU Hua-wen and ZHENG Zhong-long
摘要： 多标签学习已成为当前机器学习的研究热点。为了提高分类性能,对训练集中的噪声数据进行预处理,提出一种基于k近邻(kNN)的多标签分类去噪方法:对现有的多标签数据集进行分析后获得近似正态分布的特征,通过将噪声标记改为其k近邻标记的方法,滤去部分噪声信息,从而得到相对高质量的数据集。在MULAN平台上使用多个数据集对6种多标签分类算法进行了噪声去除前后的对比测试,实验结果表明,多标签的预处理方法有效提高了分类器的性能。此方法对于分布特征明显的数据集具有较好的适用性。
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