Results 61 to 70 of about 637,915 (182)
Efficient multi-label classification for evolving data streams [PDF]
, 2010 Many real world problems involve data which can be considered as multi-label data streams. Efficient methods exist for multi-label classification in non streaming scenarios.
Bifet, Albert +3 more
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A Novel IGBT Health Evaluation Method Based on Multi-Label Classification
IEEE Access, 2019 The IGBT health evaluation of power semiconductor devices is usually based on the threshold evaluation method, which is usually a single characteristic parameter evaluation system. This kind of evaluation method cannot reflect the internal correlation of
Ruikun Quan, Hui Li, Yaogang Hu, Pei Gao
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An Efficient Stacking Model of Multi-Label Classification Based on Pareto Optimum
IEEE Access, 2019 Nowadays, multi-label data are ubiquitous in real-world applications, in which each instance is associated with a set of labels. Multi-label learning has attracted significant attentions from researchers and plenty of algorithms have been proposed. Among
Wei Weng +4 more
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Multi-Label Feature Selection Based on High-Order Label Correlation Assumption
Entropy, 2020 Multi-label data often involve features with high dimensionality and complicated label correlations, resulting in a great challenge for multi-label learning.
Ping Zhang +3 more
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Dynamic Online Label Distribution Feature Selection Based on Label Importance and Label Correlation
Applied SciencesExisting feature selection methods mainly target single-label learning and multi-label learning, and only a few algorithms are optimized for label distribution learning.
Weiliang Chen, Xiao Sun, Fuji Ren
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Food Ingredients Recognition Through Multi-label Learning
, 2023 The ability to recognize various food-items in a generic food plate is a key determinant for an automated diet assessment system. This study motivates the need for automated diet assessment and proposes a framework to achieve this. Within this framework, we focus on one of the core functionalities to visually recognize various ingredients. To this end,
Ismail, Rameez, Yuan, Zhaorui
openaire +2 more sources
Towards Interpretable Deep Extreme Multi-Label Learning [PDF]
2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science (IRI), 2019 Many Machine Learning algorithms, such as deep neural networks, have long been criticized for being "black-boxes"-a kind of models unable to provide how it arrive at a decision without further efforts to interpret. This problem has raised concerns on model applications' trust, safety, nondiscrimination, and other ethical issues.
Kang, Yihuang +4 more
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Learning Deep Latent Spaces for Multi-Label Classification
, 2017 Multi-label classification is a practical yet challenging task in machine learning related fields, since it requires the prediction of more than one label category for each input instance.
Ko, Wei-Jen +3 more
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Large-scale Multi-label Learning with Missing Labels [PDF]
, 2013 The multi-label classification problem has generated significant interest in recent years. However, existing approaches do not adequately address two key challenges: (a) the ability to tackle problems with a large number (say millions) of labels, and (b)
Dhillon, Inderjit S. +3 more
core +2 more sources
Semantically Guided Multi-Stage Graph Learning for Inductive Multi-Label Text Classification
Journal of King Saud University: Computer and Information SciencesMost graph neural network-based multi-label text classification methods suffer from two key engineering limitations: poor generalization to unseen data due to transductive learning, and suboptimal performance caused by fixed-label graphs that fail to ...
Mingqiang Wu
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