Results 1 to 10 of about 232,106 (169)
Applied Computing and Informatics, 2015 Generating multi-label rules in associative classification (AC) from single label data sets is considered a challenging task making the number of existing algorithms for this task rare.
Neda Abdelhamid
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ACM Transactions on Knowledge Discovery from Data, 2023 Quantification, variously called supervised prevalence estimation or learning to quantify , is the supervised learning task of generating predictors of the relative frequencies (a.k.a. prevalence values ) of the classes of interest in unlabelled data samples.
Alejandro Moreo +2 more
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Malicious Vehicle Detection Using Layer-Based Paradigm and the Internet of Things
Sensors, 2023 Deep learning algorithms have a wide range of applications, including cancer diagnosis, face and speech recognition, object recognition, etc. It is critical to protect these models since any changes to them can result in serious losses in a variety of ...
Abdul Razaque +7 more
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Multi‐label learning based target detecting from multi‐frame data
IET Image Processing, 2021 In the field of target detecting, lots of progress have been made in recent years. Owing to the progress of multiple frames time series data, or video satellites, target detecting from space‐borne satellite videos has been available. However, detecting a
Mengqing Mei, Fazhi He
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Machine Learning Based Embedded Code Multi-Label Classification
IEEE Access, 2021 With the development of Internet of Things (IoT) technology, embedded based electronic devices have penetrated every corner of our daily lives. As the brain of IoT devices, embedded based micro controller unit (MCU) plays an irreplaceable role.
Yu Zhou, Suxia Cui, Yonghui Wang
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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 ...
Inder Pal Singh +4 more
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Multi-label Adversarial Perturbations [PDF]
2018 IEEE International Conference on Data Mining (ICDM), 2018 Adversarial examples are delicately perturbed inputs, which aim to mislead machine learning models towards incorrect outputs. While most of the existing work focuses on generating adversarial perturbations in multi-class classification problems, many real-world applications fall into the multi-label setting in which one instance could be associated ...
Qingquan Song +3 more
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MLCM: Multi-Label Confusion Matrix
IEEE Access, 2022 Concise and unambiguous assessment of a machine learning algorithm is key to classifier design and performance improvement. In the multi-class classification task, where each instance can only be labeled as one class, the confusion matrix is a powerful ...
Mohammadreza Heydarian +2 more
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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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Diagnostics, 2022 Background and Motivation: Cardiovascular disease (CVD) causes the highest mortality globally. With escalating healthcare costs, early non-invasive CVD risk assessment is vital. Conventional methods have shown poor performance compared to more recent and
Jasjit S. Suri +15 more
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