摘要
在线学习方法是用于大规模数据集的、高效且可扩展的机器学习算法。然而,在对多标签数据集进行特征选择时,传统的在线多标签学习方法需要访问数据集的所有特征,当数据集具有较高维度时,这种在线学习方式并不能适用于实际情景。针对多标签数据集的特征选择,在现有研究的基础上,使用二类分解策略,提出基于分解策略的多标签在线特征选择算法。该算法利用稀疏正则化和截取方法进行在线特征选择,降低计算复杂度。实验表明,算法的特征选择性能优于其他多标签在线特征选择算法。
Online learning method is an efficient and extensible machine learning algorithm for large-scale applications.However,when selecting features for multi-label datasets,the traditional online multi-label learning algorithms need to access all the features of the dataset.When the dataset has a higher dimension,this online learning method cannot be applied to the actual situation.Based on the existing research,a multi-label online feature selection algorithm based on decomposition strategy is proposed by using binary relevance strategy.This algorithm uses sparse regularization and interception methods for online feature selection to reduce computational complexity.The experimental results show that the feature selection performance of the algorithm is better than that of other multi-label online feature selection algorithms.
作者
张永伟
朱祁
吴永城
Zhang Yongwei;Zhu Qi;Wu Yongcheng(NARI Group Corporation(State Grid Electric Power Research Institute),Nanjing 210003,China;Nanjing NARI Intelligent Transport Technology Co.,Ltd.,Nanjing 210032,China)
出处
《网络安全与数据治理》
2022年第10期65-71,77,共8页
CYBER SECURITY AND DATA GOVERNANCE
基金
国家电网科技项目(52460D220001)。
关键词
特征选择
在线学习
多标签分类
二类分解策略
feature selection
online learning
multi-label classification
binary relevance strategy
