{"title":"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","authors":"Yi-Zhang Li , Fan Min","doi":"10.1016/j.knosys.2024.112568","DOIUrl":null,"url":null,"abstract":"<div><div>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 <span><span>github.com/fansmale/lsic-ps</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":null,"pages":null},"PeriodicalIF":7.2000,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Parallel–serial architecture with instance correlation label-specific features for multi-label learning\",\"authors\":\"Yi-Zhang Li , Fan Min\",\"doi\":\"10.1016/j.knosys.2024.112568\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>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 <span><span>github.com/fansmale/lsic-ps</span><svg><path></path></svg></span>.</div></div>\",\"PeriodicalId\":49939,\"journal\":{\"name\":\"Knowledge-Based Systems\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":7.2000,\"publicationDate\":\"2024-10-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Knowledge-Based Systems\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0950705124012024\",\"RegionNum\":1,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Knowledge-Based Systems","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0950705124012024","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
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.
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.