HFSA: hybrid feature selection approach to improve medical diagnostic system DOI Creative Commons
Asmaa H. Rabie, Mohammed Aldawsari, Ahmed I. Saleh

и другие.

PeerJ Computer Science, Год журнала: 2025, Номер 11, С. e2764 - e2764

Опубликована: Май 6, 2025

Thanks to the presence of artificial intelligence methods, diagnosis patients can be done quickly and accurately. This article introduces a new diagnostic system (DS) that includes three main layers called rejection layer (RL), selection (SL), (DL) accurately diagnose cases suffering from various diseases. In RL, outliers removed using genetic algorithm (GA). At same time, best features selected by feature method hybrid approach (HFSA) in SL. next step, filtered data is passed naive Bayes (NB) classifier DL give accurate diagnoses. this work, contribution represented introducing HFSA as composed two stages; fast stage (FS) (AS). FS, chi-square, filtering methodology, applied select while Hybrid Optimization Algorithm (HOA), wrapper AS features. It concluded better than other methods based on experimental results because enable different classifiers NB, K-nearest neighbors (KNN), neural network (ANN) provide maximum accuracy, precision, recall values minimum error value. Additionally, proved DS, including GA an outlier method, selection, NB mode, outperformed models.

Язык: Английский

Identifying influential nodes in weighted complex networks by considering the importance of shortest paths DOI Creative Commons
Xiaohong Wang, Zhenyu Wang

Journal Of Big Data, Год журнала: 2025, Номер 12(1)

Опубликована: Апрель 22, 2025

Язык: Английский

Процитировано

1
Enhanced anomaly detection through a Bayesian framework with a novel network merging structure learning approach DOI
Ashani Wickramasinghe, Saman Muthukumarana

International Journal of Data Science and Analytics, Год журнала: 2025, Номер unknown

Опубликована: Март 5, 2025

Язык: Английский

Процитировано

0
A polyhedral reconstruction of a 3D object from a chain code and a low-density point cloud DOI Creative Commons

Osvaldo A. Tapia-Dueñas,

Hiram H. López, Hermilo Sánchez-Cruz

и другие.

Multimedia Tools and Applications, Год журнала: 2025, Номер unknown

Опубликована: Март 27, 2025

Язык: Английский

Процитировано

0
Semi-Supervised Attribute Selection Algorithms for Partially Labeled Multiset-Valued Data DOI Creative Commons

Yuanzi He,

Jiali He, Haotian Liu

и другие.

Mathematics, Год журнала: 2025, Номер 13(8), С. 1318 - 1318

Опубликована: Апрель 17, 2025

In machine learning, when the labeled portion of data needs to be processed, a semi-supervised learning algorithm is used. A dataset with missing attribute values or labels referred as an incomplete information system. Addressing within system poses significant challenge, which can effectively tackled through application rough set theory (R-theory). However, R-theory has its limits: It fails consider frequency value and then cannot distribution appropriately. If we partially replace multiset all possible under same attribute, this results in emergence multiset-valued data. algorithm, order save time costs, large number redundant features need deleted. This study proposes selection algorithms for Initially, decision (p-MSVDIS) partitioned into two distinct systems: (l-MSVDIS) unlabeled (u-MSVDIS). Subsequently, using indistinguishable relation, distinguishable dependence function, types subset importance p-MSVDIS are defined: weighted sum l-MSVDIS u-MSVDIS determined by rate labels, considered uncertainty measurement (UM) p-MSVDIS. Next, adaptive introduced, leverage degrees importance, allowing automatic adaptation diverse rates. Finally, experiments statistical analyses conducted on 11 datasets. The outcome indicates that proposed demonstrate advantages over certain algorithms.

Язык: Английский

Процитировано

0
The ensemble of self-information-based feature selection for heterogeneous data via k-nearest neighborhood rough set model DOI
Yu Zhang,

Yonghua Lin,

Mohamed Rizon

и другие.

The Journal of Supercomputing, Год журнала: 2025, Номер 81(6)

Опубликована: Апрель 25, 2025

Язык: Английский

Процитировано

0
HFSA: hybrid feature selection approach to improve medical diagnostic system DOI Creative Commons
Asmaa H. Rabie, Mohammed Aldawsari, Ahmed I. Saleh

и другие.

PeerJ Computer Science, Год журнала: 2025, Номер 11, С. e2764 - e2764

Опубликована: Май 6, 2025

Thanks to the presence of artificial intelligence methods, diagnosis patients can be done quickly and accurately. This article introduces a new diagnostic system (DS) that includes three main layers called rejection layer (RL), selection (SL), (DL) accurately diagnose cases suffering from various diseases. In RL, outliers removed using genetic algorithm (GA). At same time, best features selected by feature method hybrid approach (HFSA) in SL. next step, filtered data is passed naive Bayes (NB) classifier DL give accurate diagnoses. this work, contribution represented introducing HFSA as composed two stages; fast stage (FS) (AS). FS, chi-square, filtering methodology, applied select while Hybrid Optimization Algorithm (HOA), wrapper AS features. It concluded better than other methods based on experimental results because enable different classifiers NB, K-nearest neighbors (KNN), neural network (ANN) provide maximum accuracy, precision, recall values minimum error value. Additionally, proved DS, including GA an outlier method, selection, NB mode, outperformed models.

Язык: Английский

Процитировано

0