Results 11 to 20 of about 7,571,754 (304)
Multi-Graph Multi-Label Learning Based on Entropy [PDF]
Entropy, 2018 Recently, Multi-Graph Learning was proposed as the extension of Multi-Instance Learning and has achieved some successes. However, to the best of our knowledge, currently, there is no study working on Multi-Graph Multi-Label Learning, where each object is
Zixuan Zhu, Yuhai Zhao
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Improving Multi-Label Learning by Correlation Embedding
Applied Sciences, 2021 In multi-label learning, each object is represented by a single instance and is associated with more than one class labels, where the labels might be correlated with each other.
Jun Huang +4 more
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Multi-label Ensemble Learning [PDF]
, 2011 Multi-label learning aims at predicting potentially multiple labels for a given instance. Conventional multi-label learning approaches focus on exploiting the label correlations to improve the accuracy of the learner by building an individual multi-label learner or a combined learner based upon a group of single-label learners.
Chuan Shi 0001 +3 more
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Automatic multi-label subject indexing in a multilingual environment [PDF]
, 2003 This paper presents an approach to automatically subject index fulltext documents with multiple labels based on binary support vector machines(SVM). The aim was to test the applicability of SVMs with a real world dataset.
Lauser, Boris, Hotho, Andreas
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Multi-Target Rough Sets and Their Approximation Computation with Dynamic Target Sets
Information, 2022 Multi-label learning has become a hot topic in recent years, attracting scholars’ attention, including applying the rough set model in multi-label learning.
Wenbin Zheng, Jinjin Li, Shujiao Liao
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On the consistency of multi-label learning
Artificial Intelligence, 2013 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Wei Gao 0008, Zhi-Hua Zhou
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Multi-label classification using ensembles of pruned sets [PDF]
, 2008 This paper presents a Pruned Sets method (PS) for multi-label classification. It is centred on the concept of treating sets of labels as single labels. This allows the classification process to inherently take into account correlations between labels. By
Pfahringer, Bernhard +5 more
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A Multi-Objective Multi-Label Feature Selection Algorithm Based on Shapley Value
Entropy, 2021 Multi-label learning is dedicated to learning functions so that each sample is labeled with a true label set. With the increase of data knowledge, the feature dimensionality is increasing.
Hongbin Dong, Jing Sun, Xiaohang Sun
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A lexicographic multi-objective genetic algorithm for multi-label correlation-based feature selection [PDF]
, 2015 This paper proposes a new Lexicographic multi-objective Genetic Algorithm for Multi-Label Correlation-based Feature Selection (LexGA-ML-CFS), which is an extension of the previous single-objective Genetic Algorithm for Multi-label Correlation-based ...
Suwimol Jungjit +3 more
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IEEE Access, 2019 Multi-label learning deals with problems in which each instance is associated with a set of labels. Most multi-label learning algorithms ignore the potential distribution differences between the training domain and the test domain in the instance space ...
Siyu Jiang +6 more
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