Machine learning based multi-label classification of single or mixed-composition urinary stones in in-vivo spectral CT.
Sign Up to like & getrecommendations! Published in 2022 at "Medical physics"
DOI: 10.1002/mp.16154
Abstract: BACKGROUND Urinary stones comprise both single and mixed compositions. Knowledge of the stone composition helps the urologists choose appropriate medical interventions for patients. The parameters from the spectral computerized tomography (CT) analysis have potential values… read more here.
Keywords: composition; multi label; label; label classification ... See more keywords
Multi-label classification of retinal lesions in diabetic retinopathy for automatic analysis of fundus fluorescein angiography based on deep learning
Sign Up to like & getrecommendations! Published in 2020 at "Graefe's Archive for Clinical and Experimental Ophthalmology"
DOI: 10.1007/s00417-019-04575-w
Abstract: Purpose To automatically detect and classify the lesions of diabetic retinopathy (DR) in fundus fluorescein angiography (FFA) images using deep learning algorithm through comparing 3 convolutional neural networks (CNNs). Methods A total of 4067 FFA… read more here.
Keywords: diabetic retinopathy; multi label; retinal lesions; lesions diabetic ... See more keywords
Stacking model of multi-label classification based on pruning strategies
Sign Up to like & getrecommendations! Published in 2018 at "Neural Computing and Applications"
DOI: 10.1007/s00521-018-3888-0
Abstract: Exploiting dependencies between the labels is the key of improving the performance of multi-label classification. In this paper, we divide the utilizing methods of label dependence into two groups from the perspective of different ways… read more here.
Keywords: label classification; multi label; feature;
LabCor: Multi-label classification using a label correction strategy
Sign Up to like & getrecommendations! Published in 2021 at "Applied Intelligence"
DOI: 10.1007/s10489-021-02674-y
Abstract: Multi-label classification is a branch of machine learning that can effectively reflect real-world problems. Among all the multi-label classification methods, stacked binary relevance (2BR) is a classic approach. Based on 2BR, a series of optimized… read more here.
Keywords: label; two level; classification; multi label ... See more keywords
Data scarcity, robustness and extreme multi-label classification
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DOI: 10.1007/s10994-019-05791-5
Abstract: The goal in extreme multi-label classification (XMC) is to learn a classifier which can assign a small subset of relevant labels to an instance from an extremely large set of target labels. The distribution of… read more here.
Keywords: label; classification; extreme multi; multi label ... See more keywords
Calibrated Multi-label Classification with Label Correlations
Sign Up to like & getrecommendations! Published in 2018 at "Neural Processing Letters"
DOI: 10.1007/s11063-018-9925-2
Abstract: Multi-label classification is a special learning task where each instance may be associated with multiple labels simultaneously. There are two main challenges: (a) discovering and exploiting the label correlations automatically, and (b) separating the relevant… read more here.
Keywords: label; classification label; multi label; label correlations ... See more keywords
Reduced-rank multi-label classification
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DOI: 10.1007/s11222-015-9615-0
Abstract: Multi-label classification is a natural generalization of the classical binary classification for classifying multiple class labels. It differs from multi-class classification in that the multiple class labels are not exclusive. The key challenge is to… read more here.
Keywords: reduced rank; label classification; classification; multi label ... See more keywords
Towards multi-label classification: Next step of machine learning for microbiome research
Sign Up to like & getrecommendations! Published in 2021 at "Computational and Structural Biotechnology Journal"
DOI: 10.1016/j.csbj.2021.04.054
Abstract: Machine learning (ML) has been widely used in microbiome research for biomarker selection and disease prediction. By training microbial profiles of samples from patients and healthy controls, ML classifiers constructs data models by community features… read more here.
Keywords: label classification; multi label; machine learning; microbiome research ... See more keywords
Selecting label-dependent features for multi-label classification
Sign Up to like & getrecommendations! Published in 2017 at "Neurocomputing"
DOI: 10.1016/j.neucom.2016.08.122
Abstract: Abstract An instance is often represented from different aspects (views or modalities), which leads to high-dimensional features and even multiple labels. In this paper, we focus on the feature selection problem in multi-label classification, for… read more here.
Keywords: label dependent; label classification; multi label; dependent features ... See more keywords
Local positive and negative correlation-based k-labelsets for multi-label classification
Sign Up to like & getrecommendations! Published in 2018 at "Neurocomputing"
DOI: 10.1016/j.neucom.2018.08.035
Abstract: Abstract Multi-label classification has attracted great attention from different domains. RAkEL (Random k-label sets) is an effective high-order multi-label learning approach. However, this method exploits label correlations randomly, which cannot make full use of label… read more here.
Keywords: classification; positive negative; multi label; label correlations ... See more keywords
Conditional entropy based classifier chains for multi-label classification
Sign Up to like & getrecommendations! Published in 2019 at "Neurocomputing"
DOI: 10.1016/j.neucom.2019.01.039
Abstract: Abstract In many real-world problems, data samples are simultaneously associated with multiple labels, instead of a single label. Multi-label classification deals with such problems, and has extensive applications in many fields. Among the many methods… read more here.
Keywords: classification; multi label; classifier chains; conditional entropy ... See more keywords