采用实例相关标签特定特征的并行串行架构,用于多标签学习

IF 7.2 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Yi-Zhang Li , Fan Min
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引用次数: 0

摘要

特征提取在捕捉数据相关性,从而提高多标签学习模型的性能方面起着至关重要的作用。流行的方法主要包括特征空间操作技术(如递归特征消除)和特征替代技术(如特定标签特征提取)。然而,前者没有利用标签信息,后者没有考虑实例之间的相关性。在本研究中,我们提出了一种标签特定特征提取方法,在并行串行架构(LSIC-PS)下通过联合损失函数嵌入实例相关性。我们的方法包含三项主要技术。首先,我们采用并行同构网络来提取特定标签特征,并将其直接集成到串行网络中以增强标签相关性。其次,我们引入实例相关性来指导并行网络中的特征提取,利用来自其他实例的标签信息来提高泛化能力。第三,我们设计了一种参数设置策略来控制新的联合损失函数,使其实例相关比例适应不同的数据集。我们在 16 个广泛使用的数据集上进行了实验,并将我们的方法与 12 种流行算法的结果进行了比较。在八个评估指标中,LSIC-PS 在多标签学习方面表现出了最先进的性能。源代码可在 github.com/fansmale/lsic-ps 上获取。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Parallel–serial architecture with instance correlation label-specific features for multi-label learning
Feature extraction plays a crucial role in capturing data correlations, thereby improving the performance of multi-label learning models. Popular approaches mainly include feature space manipulation techniques, such as recursive feature elimination, and feature alternative techniques, such as label-specific feature extraction. However, the former does not utilize label information, while the latter does not consider correlation among instances. In this study, we propose a label-specific feature extraction approach embedding instance correlation by a joint loss function under a parallel–serial architecture (LSIC-PS). Our approach incorporates three main techniques. First, we employ a parallel isomorphic network to extract label-specific features, which are directly integrated into a serial network to enhance label correlation. Second, we introduce instance correlation to guide feature extraction in parallel networks, leveraging label information from other instances to improve generalization. Third, we design a parameter-setting strategy to control a new joint loss function, adapting its instance correlation proportion to different datasets. We conduct experiments on sixteen widely used datasets and compare the results of our approach with those of twelve popular algorithms. Across eight evaluation metrics, LSIC-PS demonstrates state-of-art performance in multi-label learning. The source code is available at github.com/fansmale/lsic-ps.
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来源期刊
Knowledge-Based Systems
Knowledge-Based Systems 工程技术-计算机:人工智能
CiteScore
14.80
自引率
12.50%
发文量
1245
审稿时长
7.8 months
期刊介绍: Knowledge-Based Systems, an international and interdisciplinary journal in artificial intelligence, publishes original, innovative, and creative research results in the field. It focuses on knowledge-based and other artificial intelligence techniques-based systems. The journal aims to support human prediction and decision-making through data science and computation techniques, provide a balanced coverage of theory and practical study, and encourage the development and implementation of knowledge-based intelligence models, methods, systems, and software tools. Applications in business, government, education, engineering, and healthcare are emphasized.
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