Two‐stage‐neighborhood‐based multilabel classification for incomplete data with missing labels
Sign Up to like & getrecommendations! Published in 2022 at "International Journal of Intelligent Systems"
DOI: 10.1002/int.22861
Abstract: In recent years, it has been difficult for multilabel classification to obtain complete multilabel data in real‐world applications, and even a large number of labels for training samples are randomly missed. As a result, the… read more here.
Keywords: classification; multilabel classification; two stage; missing labels ... See more keywords
Multi-label Learning with Missing Labels Using Mixed Dependency Graphs
Sign Up to like & getrecommendations! Published in 2018 at "International Journal of Computer Vision"
DOI: 10.1007/s11263-018-1085-3
Abstract: This work focuses on the problem of multi-label learning with missing labels (MLML), which aims to label each test instance with multiple class labels given training instances that have an incomplete/partial set of these labels… read more here.
Keywords: label; multi label; missing labels; graph ... See more keywords
Explainable Deep Learning Approach for Multilabel Classification of Antimicrobial Resistance With Missing Labels
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DOI: 10.1109/access.2022.3216896
Abstract: Predicting Antimicrobial Resistance (AMR) from genomic sequence data has become a significant component of overcoming the AMR challenge, especially given its potential for facilitating more rapid diagnostics and personalised antibiotic treatments. With the recent advances… read more here.
Keywords: missing labels; deep learning; genomic sequence; antimicrobial resistance ... See more keywords
Rectified Classifier Chains for Prediction of Antibiotic Resistance From Multi-Labelled Data With Missing Labels
Sign Up to like & getrecommendations! Published in 2022 at "IEEE/ACM Transactions on Computational Biology and Bioinformatics"
DOI: 10.1109/tcbb.2022.3148577
Abstract: Predicting Antimicrobial Resistance (AMR) from genomic data has important implications for human and animal healthcare, and especially given its potential for more rapid diagnostics and informed treatment choices. With the recent advances in sequencing technologies,… read more here.
Keywords: rcc; amr prediction; missing labels; resistance ... See more keywords
Topic-Based Algorithm for Multilabel Learning With Missing Labels
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Transactions on Neural Networks and Learning Systems"
DOI: 10.1109/tnnls.2018.2874434
Abstract: In multilabel learning (MLL), each instance can be assigned by several concepts simultaneously from a class dictionary. Usually, labels in the class dictionary have semantic correlations and semantic hierarchy. Instances can be categorized into different… read more here.
Keywords: label; wise; missing labels; topic ... See more keywords