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Information Processing and Management, 2019 © 2018 Elsevier Ltd Multi-label text categorization refers to the problem of assigning each document to a subset of categories by means of multi-label learning algorithms.
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Multi-label Learning with Missing Labels
2014 22nd International Conference on Pattern Recognition, 2014In multi-label learning, each sample can be assigned to multiple class labels simultaneously. In this work, we focus on the problem of multi-label learning with missing labels (MLML), where instead of assuming a complete label assignment is provided for each sample, only partial labels are assigned with values, while the rest are missing or not ...
Baoyuan Wu +4 more
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Metric Learning for Multi-label Classification
2021This paper proposes an approach for multi-label classification based on metric learning. The approach has been designed to deal with general classification problems, without any assumption on the specific kind of data used (images, text, etc.) or semantic meaning assigned to labels (tags, categories, etc.). It is based on clustering and metric learning
Marco Brighi +2 more
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Multi-label Quadruplet Dictionary Learning
2020The explosion of the label space degrades the performance of the classic multi-class learning models. Label space dimension reduction (LSDR) is developed to reduce the dimension of the label space by learning a latent representation of both the feature space and label space.
Jiayu Zheng +2 more
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A multi-label incremental learning algorithm
2012 9th International Conference on Fuzzy Systems and Knowledge Discovery, 2012A multi-label incremental learning algorithm based on hyper ellipsoidal is proposed. To every class, the smallest hyper ellipsoidal that surrounds most samples of the class is structured, which can divide the class samples from others. In the process of incremental learning, only are the hyper ellipsoidals that its class exist in new incremental ...
Yuping Qin +3 more
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A Unified Multi-label Relationship Learning
2019 14th International Conference on Computer Science & Education (ICCSE), 2019Multi-label learning belongs to the class of supervised learning wherein each sample is represented by a single instance and is associated with a set of relevant labels. Many realworld applications like medical diagnosis and image classification involve multi-label classification wherein label correlations are essential to the performance of the ...
Reshma Rastogi +3 more
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Robust Extreme Multi-label Learning
Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016Tail labels in the multi-label learning problem undermine the low-rank assumption. Nevertheless, this problem has rarely been investigated. In addition to using the low-rank structure to depict label correlations, this paper explores and exploits an additional sparse component to handle tail labels behaving as outliers, in order to make the classical ...
Chang Xu 0002, Dacheng Tao, Chao Xu 0006
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Long Tail Multi-label Learning
2019 IEEE Second International Conference on Artificial Intelligence and Knowledge Engineering (AIKE), 2019Multi-label learning is an activity research area that many methods arise recently to solve this problem. However, according to the results of current researches, the class imbalance which appears in the most of labels makes the network unable to be trained.
Mengqi Yuan, Jinke Xu, Zhongnian Li
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Unconstrained Multimodal Multi-Label Learning
IEEE Transactions on Multimedia, 2015Multimodal learning has been mostly studied by assuming that multiple label assignments are independent of each other and all the modalities are available. In this paper, we consider a more general problem where the labels contain dependency relationships and some modalities are likely to be missing.
Yan Huang 0008 +2 more
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2018
The defining characteristic of multi-label as opposed to single-label data is that each instance can belong to several classes at once. The multi-label classification task is to predict all relevant labels of a target instance. This chapter presents and experimentally evaluates our FRONEC method, the Fuzzy Rough NEighbourhood Consensus.
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The defining characteristic of multi-label as opposed to single-label data is that each instance can belong to several classes at once. The multi-label classification task is to predict all relevant labels of a target instance. This chapter presents and experimentally evaluates our FRONEC method, the Fuzzy Rough NEighbourhood Consensus.
openaire +1 more source