{"title":"Interval type-2 fuzzy neural networks for multi-label classification","authors":"Dayong Tian , Feifei Li , Yiwen Wei","doi":"10.1016/j.knosys.2025.113014","DOIUrl":null,"url":null,"abstract":"<div><div>Prediction of multi-dimensional labels plays an important role in machine learning problems. We discovered that traditional binary labels could not capture the contents and relationships in an instance. Hence, we propose a multi-label classification model based on interval type-2 fuzzy logic.</div><div>In the proposed model, we use a deep neural network to predict an instance’s type-1 fuzzy membership and another to predict the membership’s fuzzifiers, resulting in interval type-2 fuzzy memberships. We also propose a loss function for determining the similarities between binary labels in datasets and interval type-2 fuzzy memberships generated by our model. The experiments validate that our approach outperforms baselines in multi-label classification benchmarks.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"312 ","pages":"Article 113014"},"PeriodicalIF":7.2000,"publicationDate":"2025-02-05","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/S0950705125000620","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}
引用次数: 0
Abstract
Prediction of multi-dimensional labels plays an important role in machine learning problems. We discovered that traditional binary labels could not capture the contents and relationships in an instance. Hence, we propose a multi-label classification model based on interval type-2 fuzzy logic.
In the proposed model, we use a deep neural network to predict an instance’s type-1 fuzzy membership and another to predict the membership’s fuzzifiers, resulting in interval type-2 fuzzy memberships. We also propose a loss function for determining the similarities between binary labels in datasets and interval type-2 fuzzy memberships generated by our model. The experiments validate that our approach outperforms baselines in multi-label classification benchmarks.
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.