Robust semi-supervised multi-label feature selection based on shared subspace and manifold learning

Information Sciences, Год журнала: 2024, Номер 699, С. 121800 - 121800

Опубликована: Дек. 27, 2024

Язык: Английский

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Multi-level information fusion for missing multi-label learning based on stochastic concept clustering

Information Fusion, Год журнала: 2024, Номер 115, С. 102775 - 102775

Опубликована: Ноя. 2, 2024

Язык: Английский

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A robust multi-label feature selection based on label significance and fuzzy entropy

International Journal of Approximate Reasoning, Год журнала: 2024, Номер 176, С. 109310 - 109310

Опубликована: Ноя. 7, 2024

Язык: Английский

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An integrated data mining algorithms and meta-heuristic technique to predict the readmission risk of diabetic patients

Healthcare Analytics, Год журнала: 2023, Номер 5, С. 100292 - 100292

Опубликована: Дек. 16, 2023

Reducing hospital readmission rate is a significant challenge in the healthcare industry for managers and policymakers seeking to improve lower costs. This study integrates data mining meta-heuristic techniques predict early probability of diabetic patients within 30 days discharge. The research dataset was obtained from UC Irvine Machine Learning Repository, including 101765 instances with 50 features representing patient outcomes, collected 130 US hospitals. After preprocessing, cleansing, sampling, normalization, Chi-square analysis done confirm rank 20 identified factors affecting risk. As algorithms' performance could vary based on features' characteristics, several classification algorithms, Random Forest (RF), Neural Network (NN), Support Vector (SVM), are employed. Moreover, Genetic Algorithm (GA) integrated into SVM algorithm, called GA-SVM, hyper-parameter tuning increasing prediction accuracy. models evaluated using accuracy, recall, precision, f-measure metrics. results indicate that accuracy RF, SVM, NN calculated respectively as 74.04 %, 73.52 72.40 70.44 %. Using GA adjust c gamma hyper-parameters led 1.12 % increase In response demand considering poor conditions, particularly during epidemics, these findings point out potential benefits more tailored methodology managing patients.

Язык: Английский

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Fuzzy Neighborhood-based Partial Label Feature Selection via Label Iterative Disambiguation

International Journal of Approximate Reasoning, Год журнала: 2025, Номер unknown, С. 109358 - 109358

Опубликована: Янв. 1, 2025

Язык: Английский

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Multi-label feature selection with missing features by tolerance implication granularity information and symmetric coupled discriminant weight

Pattern Recognition, Год журнала: 2025, Номер unknown, С. 111365 - 111365

Опубликована: Янв. 1, 2025

Язык: Английский

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Semi-supervised multi-label feature selection combining nonlinear manifold structure and minimizing group sparse redundant correlation

Expert Systems with Applications, Год журнала: 2025, Номер 273, С. 126844 - 126844

Опубликована: Фев. 17, 2025

Язык: Английский

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Granular Ball-based Partial Label Feature Selection via Fuzzy Correlation and Redundancy

Information Sciences, Год журнала: 2025, Номер unknown, С. 122047 - 122047

Опубликована: Март 1, 2025

Язык: Английский

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Deep label relevance and label ambiguity based multi-label feature selection for text classification

Engineering Applications of Artificial Intelligence, Год журнала: 2025, Номер 148, С. 110403 - 110403

Опубликована: Март 8, 2025

Язык: Английский

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Compound fault diagnosis method of rotating machinery using multi-view multi-label feature selection based on label compression and local label correlation

Advanced Engineering Informatics, Год журнала: 2025, Номер 65, С. 103310 - 103310

Опубликована: Апрель 4, 2025

Язык: Английский

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