Interval type-2 fuzzy neural networks for multi-label classification

IF 7.2 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Knowledge-Based Systems Pub Date : 2025-02-05 DOI:10.1016/j.knosys.2025.113014
Dayong Tian , Feifei Li , Yiwen Wei
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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.
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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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