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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 +9 more
europepmc +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
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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 ...
Singh, Inder Pal +4 more
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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
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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
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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 +5 more
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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
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Weak Label Feature Selection Method Based on Neighborhood Rough Sets and Relief [PDF]
Jisuanji kexue, 2022 In multi-label learning and classification, existing feature selection algorithms based on neighborhood rough sets will use classification margin of samples as the neighborhood radius.However, when the margin is too large, the classification may be ...
SUN Lin, HUANG Miao-miao, XU Jiu-cheng
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Classifying emotions in Stack Overflow and JIRA using a multi-label approach [PDF]
, 2020 A forum or social media post can express multiple emotions, such as love, joy or anger. Emotion classification has been proven useful for measuring aspects such as user satisfaction.
BESSIS, NIKOLAOS +2 more
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MULTI-LABEL RANKING METHOD BASED ON POSITIVE CLASS CORRELATIONS
Jordanian Journal of Computers and Information Technology, 2020 Multi-label classification is a general type of classification that has attracted many researchers in the last two decades due to its applicability to many modern domains, such as scene classification, bioinformatics and text classification, among others.
Raed Alazaidah +3 more
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