Results 41 to 50 of about 637,915 (182)
Representation Learning With Dual Autoencoder for Multi-Label Classification
IEEE Access, 2021 Multi-label classification aims to deal with the problem that an object may be associated with one or more labels, which is a more difficult task due to the complex nature of multi-label data. The crucial problem of multi-label classification is the more
Yi Zhu +5 more
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Extreme Learning Machine for Multi-Label Classification
Entropy, 2016 Extreme learning machine (ELM) techniques have received considerable attention in the computational intelligence and machine learning communities because of the significantly low computational time required for training new classifiers.
Xia Sun +5 more
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Groupwise Ranking Loss for Multi-Label Learning
IEEE Access, 2020 This work studies multi-label learning (MLL), where each instance is associated with a subset of positive labels. For each instance, a good multi-label predictor should encourage the predicted positive labels to be close to its ground-truth positive ones.
Yanbo Fan +5 more
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Active learning for hierarchical multi-label classification [PDF]
Data Mining and Knowledge Discovery, 2020 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Nakano, Felipe Kenji +2 more
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Multi-Label Learning via Feature and Label Space Dimension Reduction
IEEE Access, 2020 In multi-label learning, each object belongs to multiple class labels simultaneously. In the data explosion age, the size of data is often huge, i.e., large number of instances, features and class labels.
Jun Huang +4 more
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Optimized Ranking Algorithm Based on Margin Criterion for Multi-Label Learning [PDF]
Jisuanji gongcheng, 2020 For classification problems in multi-label learning,the algorithm adaptation methods that transform them into a ranking problem and rank the output labels according to their relevance to the examples have made great success.This paper proposes a multi ...
JIN Yazhou, ZHANG Zhengjun, YAN Zihan, WANG Yaping
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Similarity-based Multi-label Learning [PDF]
2018 International Joint Conference on Neural Networks (IJCNN), 2018 Multi-label classification is an important learning problem with many applications. In this work, we propose a principled similarity-based approach for multi-label learning called SML. We also introduce a similarity-based approach for predicting the label set size.
Rossi, Ryan A. +3 more
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Decision Support System for Medical Diagnosis Utilizing Imbalanced Clinical Data
Applied Sciences, 2018 The clinical decision support system provides an automatic diagnosis of human diseases using machine learning techniques to analyze features of patients and classify patients according to different diseases.
Huirui Han +3 more
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Multi-Label Classification Algorithm Based on Embedded Feature Extraction [PDF]
Jisuanji gongcheng, 2019 Dimensionality reduction and feature selection methods based on single-label classification cannot be directly applied to multi-label learning.If a multi-label learning problem is composed into multiple independent single-label learning problems to ...
WANG Xiaoying, XIE Jun, TAO Xingliu, SHAO Dongsheng, WANG Zhong
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Multi-label Class-imbalanced Action Recognition in Hockey Videos via 3D Convolutional Neural Networks [PDF]
, 2018 Automatic analysis of the video is one of most complex problems in the fields of computer vision and machine learning. A significant part of this research deals with (human) activity recognition (HAR) since humans, and the activities that they perform ...
Hussain, Rasheed +4 more
core +3 more sources