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Multi‑label classification of biomedical data. [PDF]

Med Int (Lond)
Biomedical datasets constitute a rich source of information, containing multivariate data collected during medical practice. In spite of inherent challenges, such as missing or imbalanced data, these types of datasets are increasingly utilized as a basis for the construction of predictive machine-learning models.
Diakou I, Iliopoulos E, Papakonstantinou E, Dragoumani K, Yapijakis C, Iliopoulos C, Spandidos DA, Chrousos GP, Eliopoulos E, Vlachakis D.   +9 more
europepmc   +3 more sources

Application of Label Correlation in Multi-Label Classification: A Survey

Applied Sciences
Multi-Label Classification refers to the classification task where a data sample is associated with multiple labels simultaneously, which is widely used in text classification, image classification, and other fields. Different from the traditional single-
Shan Huang, Wenlong Hu, Bin Lu, Qiang Fan, Xinyao Xu, Xiaolei Zhou, Hao Yan   +6 more
doaj   +3 more sources

Review on Multi-lable Classification [PDF]

Jisuanji kexue yu tansuo, 2023
Multi-label classification refers to the classification problem where multiple labels may coexist in a single sample. It has been widely applied in fields such as text classification, image classification, music and video classification.
LI Dongmei, YANG Yu, MENG Xianghao, ZHANG Xiaoping, SONG Chao, ZHAO Yufeng
doaj   +1 more source

Compact learning for multi-label classification [PDF]

Pattern Recognition, 2021
Multi-label classification (MLC) studies the problem where each instance is associated with multiple relevant labels, which leads to the exponential growth of output space. MLC encourages a popular framework named label compression (LC) for capturing label dependency with dimension reduction.
Jiaqi Lv, Tianran Wu, Cheng-Lun Peng, Yunpeng Liu, Ning Xu 0009, Xin Geng 0001   +5 more
openaire   +3 more sources

Label Clustering for a Novel Problem Transformation in Multi-label Classification [PDF]

Journal of Universal Computer Science, 2020
Document classification is a large body of search, many approaches were proposed for single label and multi-label classification. We focus on the multi-label classification more precisely those methods that transformation multi-label classification into ...
Smail Sellah, Vincent Hilaire
doaj   +3 more sources

Multi-Label Deepfake Classification

2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), 2023
In this paper, we investigate the suitability of current multi-label classification approaches for deepfake detection. With the recent advances in generative modeling, new deepfake detection methods have been proposed. Nevertheless, they mostly formulate this topic as a binary classification problem, resulting in poor explainability capabilities ...
Inder Pal Singh, Nesryne Mejri, Van Dat Nguyen, Enjie Ghorbel, Djamila Aouada   +4 more
openaire   +2 more sources

Study and Evaluation of Spiking Neural Network Model Based on Bee Colony Optimization [PDF]

Jisuanji kexue, 2023
In order to improve the training ability of Spiking neural network,this paper takes multi-label classification problem as the research breakthrough point and adopts bee colony algorithm to optimize the model.There are many neural network models based on ...
MA Weiwei, ZHENG Qinhong, LIU Shanshan
doaj   +1 more source

Multi-label Image Classification Algorithm Based on Multi-scale Attention and Graph Model [PDF]

Jisuanji gongcheng, 2022
As an important research direction in the field of computer vision, multi-label image classification is widely used in recognition, detection, and other applications.Existing multi-label image classification methods cannot effectively use label ...
ZHU Xudong, XIONG Yun
doaj   +1 more source

Multi-Label Classification with Label Clusters

Knowledge and Information Systems, 2023
Abstract Multi-Label Classification is the task of simultaneously predicting a set of labels for an instance. Typically, two approaches are used: global, which trains a single classifier to deal with all classes at once, and local, which divides the problem into many binary problems.
Elaine Cecília Gatto, Mauri Ferrandin, Ricardo Cerri   +2 more
openaire   +2 more sources

Classifier Chains for Multi-label Classification [PDF]

Machine Learning, 2009
The widely known binary relevance method for multi-label classification, which considers each label as an independent binary problem, has been sidelined in the literature due to the perceived inadequacy of its label-independence assumption. Instead, most current methods invest considerable complexity to model interdependencies between labels.
Jesse Read, Bernhard Pfahringer, Geoff Holmes 0001, Eibe Frank   +3 more
openaire   +5 more sources