{"title":"通过拆分多重结构化支持向量机进行大规模结构化输出分类","authors":"Chun-Na Li;Yi Li;Yuan-Hai Shao","doi":"10.1109/TETCI.2024.3360339","DOIUrl":null,"url":null,"abstract":"Structured support vector machine (SSVM) is an effective method on coping with problems involving complex outputs such as multiple dependent output variables and structured output spaces. However, its training process is very time consuming for large-scale data with complex structure and many classes. In this paper, to improve the efficiency of SSVM, we propose a multiple structured support vector machine (MSSVM) for structured output classification via the idea of splitting large into small. By constructing novel classification loss for each class, MSSVM solves a series of smaller optimization problems rather than one large-size optimization problem in SSVM. Therefore, MSSVM greatly reduces the training speed of SSVM. In addition, the structured output label information and discriminative information are embedded in the introduced losses in a simple but effective way. Experiments on multiclass classification, ordinal regression and hierarchical classification datasets demonstrate the efficiency and effectiveness of the proposed MSSVM.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":null,"pages":null},"PeriodicalIF":5.3000,"publicationDate":"2024-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Large-Scale Structured Output Classification via Multiple Structured Support Vector Machine by Splitting\",\"authors\":\"Chun-Na Li;Yi Li;Yuan-Hai Shao\",\"doi\":\"10.1109/TETCI.2024.3360339\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Structured support vector machine (SSVM) is an effective method on coping with problems involving complex outputs such as multiple dependent output variables and structured output spaces. However, its training process is very time consuming for large-scale data with complex structure and many classes. In this paper, to improve the efficiency of SSVM, we propose a multiple structured support vector machine (MSSVM) for structured output classification via the idea of splitting large into small. By constructing novel classification loss for each class, MSSVM solves a series of smaller optimization problems rather than one large-size optimization problem in SSVM. Therefore, MSSVM greatly reduces the training speed of SSVM. In addition, the structured output label information and discriminative information are embedded in the introduced losses in a simple but effective way. Experiments on multiclass classification, ordinal regression and hierarchical classification datasets demonstrate the efficiency and effectiveness of the proposed MSSVM.\",\"PeriodicalId\":13135,\"journal\":{\"name\":\"IEEE Transactions on Emerging Topics in Computational Intelligence\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":5.3000,\"publicationDate\":\"2024-02-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Emerging Topics in Computational Intelligence\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10433865/\",\"RegionNum\":3,\"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":"IEEE Transactions on Emerging Topics in Computational Intelligence","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10433865/","RegionNum":3,"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}
Large-Scale Structured Output Classification via Multiple Structured Support Vector Machine by Splitting
Structured support vector machine (SSVM) is an effective method on coping with problems involving complex outputs such as multiple dependent output variables and structured output spaces. However, its training process is very time consuming for large-scale data with complex structure and many classes. In this paper, to improve the efficiency of SSVM, we propose a multiple structured support vector machine (MSSVM) for structured output classification via the idea of splitting large into small. By constructing novel classification loss for each class, MSSVM solves a series of smaller optimization problems rather than one large-size optimization problem in SSVM. Therefore, MSSVM greatly reduces the training speed of SSVM. In addition, the structured output label information and discriminative information are embedded in the introduced losses in a simple but effective way. Experiments on multiclass classification, ordinal regression and hierarchical classification datasets demonstrate the efficiency and effectiveness of the proposed MSSVM.
期刊介绍:
The IEEE Transactions on Emerging Topics in Computational Intelligence (TETCI) publishes original articles on emerging aspects of computational intelligence, including theory, applications, and surveys.
TETCI is an electronics only publication. TETCI publishes six issues per year.
Authors are encouraged to submit manuscripts in any emerging topic in computational intelligence, especially nature-inspired computing topics not covered by other IEEE Computational Intelligence Society journals. A few such illustrative examples are glial cell networks, computational neuroscience, Brain Computer Interface, ambient intelligence, non-fuzzy computing with words, artificial life, cultural learning, artificial endocrine networks, social reasoning, artificial hormone networks, computational intelligence for the IoT and Smart-X technologies.