MNAR-k-means constrains imputation values in k-means for magnitude-decaying MNAR missingness and establishes statistical consistency of the resulting cluster centers to those of fully observed data.
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2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
A deep and handcrafted feature fusion model detects pediatric congenital heart disease from phonocardiograms with 92% accuracy, 91% sensitivity, and 96% AUROC on a patient-wise held-out test set from 751 subjects.
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MNAR-$k$-means: A $k$-means Clustering for Data Missing Not at Random with Magnitude-Decaying Probability
MNAR-k-means constrains imputation values in k-means for magnitude-decaying MNAR missingness and establishes statistical consistency of the resulting cluster centers to those of fully observed data.
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Automated detection of pediatric congenital heart disease from phonocardiograms using deep and handcrafted feature fusion
A deep and handcrafted feature fusion model detects pediatric congenital heart disease from phonocardiograms with 92% accuracy, 91% sensitivity, and 96% AUROC on a patient-wise held-out test set from 751 subjects.