首页|Learning Label Correlations for Multi-Label Online Passive Aggressive Classification Algorithm
Learning Label Correlations for Multi-Label Online Passive Aggressive Classification Algorithm
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
万方数据
维普
Label correlations are an essential technique for data mining that solves the possible correlation problem between different la-bels in multi-label classification.Although this technique is widely used in multi-label classification problems,batch learning deals with most issues,which consumes a lot of time and space resources.Unlike traditional batch learning methods,online learning represents a promising family of efficient and scalable machine learning algorithms for large-scale datasets.However,existing online learning research has done little to consider correlations between labels.On the basis of existing research,this paper proposes a multi-label online learning algorithm based on label correlations by maximizing the interval between related labels and unrelated labels in multi-label samples.We evaluate the performance of the proposed algorithm on several public datasets.Experiments show the effectiveness of our algorithm.
万方数据
维普
