为多标签在线被动进取分类算法学习标签相关性

Q3 Multidisciplinary
Yongwei Zhang
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引用次数: 0

摘要

标签相关性是数据挖掘的一项基本技术,可以解决多标签分类中不同标签之间可能存在的相关性问题。虽然这种技术在多标签分类问题中得到了广泛应用,但批量学习处理的问题居多,耗费了大量的时间和空间资源。与传统的批量学习方法不同,在线学习是一种很有前途的高效、可扩展的机器学习算法,适用于大规模数据集。然而,现有的在线学习研究很少考虑标签之间的相关性。在现有研究的基础上,本文提出了一种基于标签相关性的多标签在线学习算法,即最大化多标签样本中相关标签与不相关标签之间的间隔。我们在多个公开数据集上评估了所提算法的性能。实验证明了我们算法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Learning Label Correlations for Multi-Label Online Passive Aggressive Classification Algorithm
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.
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来源期刊
Wuhan University Journal of Natural Sciences
Wuhan University Journal of Natural Sciences Multidisciplinary-Multidisciplinary
CiteScore
0.40
自引率
0.00%
发文量
2485
期刊介绍: Wuhan University Journal of Natural Sciences aims to promote rapid communication and exchange between the World and Wuhan University, as well as other Chinese universities and academic institutions. It mainly reflects the latest advances being made in many disciplines of scientific research in Chinese universities and academic institutions. The journal also publishes papers presented at conferences in China and abroad. The multi-disciplinary nature of Wuhan University Journal of Natural Sciences is apparent in the wide range of articles from leading Chinese scholars. This journal also aims to introduce Chinese academic achievements to the world community, by demonstrating the significance of Chinese scientific investigations.
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