Learning Label Correlations for Multi-Label Online Passive Aggressive Classification Algorithm  

作　　者：ZHANG Yongwei 

机构地区：[1]Nanjing NARI Intelligent Transport Technology Co.,Ltd.,Nanjing 210061,Jiangsu,China

出　　处：《Wuhan University Journal of Natural Sciences》2024年第1期51-58,共8页武汉大学学报（自然科学英文版）

基　　金：Supported by the State Grid Technology Item(52460D230002)。

摘　　要：Label correlations are an essential technique for data mining that solves the possible correlation problem between different labels 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.

关 键 词：label correlations passive aggressive multi-label classification online learning 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]