Fuzzy Label: From Concept to Its Application in Label Learning
Chenxi Luoa, Zhuangzhuang Zhaoa, Zhaohong Denga, Te Zhangb

TL;DR
This paper introduces fuzzy labels based on fuzzy set theory to better represent uncertainty in label annotations, enhancing the performance of label learning models in practical, ambiguous scenarios.
Contribution
It proposes a novel fuzzy labeling method and fuzzy-label-enhanced algorithms for single-label and multi-label learning, improving expressiveness and effectiveness.
Findings
Fuzzy labels better capture real-world label uncertainty.
Fuzzy-label-enhanced algorithms outperform traditional methods.
Significant performance improvements demonstrated in experiments.
Abstract
Label learning is a fundamental task in machine learning that aims to construct intelligent models using labeled data, encompassing traditional single-label and multi-label classification models. Traditional methods typically rely on logical labels, such as binary indicators (e.g., "yes/no") that specify whether an instance belongs to a given category. However, in practical applications, label annotations often involve significant uncertainty due to factors such as data noise, inherent ambiguity in the observed entities, and the subjectivity of human annotators. Therefore, representing labels using simplistic binary logic can obscure valuable information and limit the expressiveness of label learning models. To overcome this limitation, this paper introduces the concept of fuzzy labels, grounded in fuzzy set theory, to better capture and represent label uncertainty. We further propose…
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Taxonomy
TopicsText and Document Classification Technologies · Machine Learning and Data Classification · Sentiment Analysis and Opinion Mining
