一种加权的ML-kNN下班了图片算法
作者：王春艳
来源：《电脑知识与技术》姓赵2012年第知行统一原则04期
摘要: ML-kNN表达高兴的词语算法利用贝叶斯概率修改传统的kNN算法以解决多标签问题，但这种基于概率统计的方法对覆盖率低的标签容易造成误判。因此，该文提出了一种加权ML-kNN牧羊姑娘打一字算法，将样本与邻居之间的距离转化为权值来改这种误判。在三个基准数据集上进行对比实验，利用七个标准对其进行评测。实验结果表明，该加权ML-kNN算法整体上优于ML-kNN怎么种向日葵>十大名牌算法。
关键词天秤男和白羊女:多标签学习; ML-kNN;距离加权;加权ML-kNN
中图分类号：TP18文献标识码：A文章编号：1009-3044(2012)04-0816-03
A Novel Weighted Multi-label kNN Algorithm
WANG Chun-yan
(Department of Computer Science and Technology, Tongji University, Shanghai 201804, China)
Abstract: ML-kNN modifies kNN by combining Bayesian probability to solve multi-label problem. However, bad on probability statis? tics, ML-kNN doesn"t tend to assign tho labels with low occurrence frequency for samples. Thus we propod a novel weighted ML-kNN algorithm by concerning distances between a sample and its neighbors. We evaluated its performance on three benchmark datas? ets with ven metrics. The experiment results show that the weighted ML-kNN algorithm has better performance than ML-kNN on the whole.
