Results 31 to 40 of about 1,216,046 (262)

Finding k-Dominant G-Skyline Groups on High Dimensional Data

IEEE Access, 2018
Skyline query retrieves a set of skyline points which are not dominated by any other point and has attracted wide attention in database community. Recently, an important variant G-Skyline is developed. It aims to return optimal groups of points. However,
Kaiqi Zhang, Hong Gao, Xixian Han, Jinbao Wang   +3 more
doaj   +1 more source

A classification method for high‐dimensional imbalanced multi‐classification data

Electronics Letters, 2023
High‐dimensional imbalanced multi‐classification problems (HDIMCPs) occur frequently in engineering applications such as medical detection, item classification, and email classification.
Mengmeng Li, Qibin Zheng, Yi Liu, Gengsong Li, Wei Qin, Xiaoguang Ren   +5 more
doaj   +1 more source

Feature Selection in High Dimensional Biomedical Data Based on BF-SFLA

Frontiers in Neuroscience, 2022
High-dimensional biomedical data contained many irrelevant or weakly correlated features, which affected the efficiency of disease diagnosis. This manuscript presented a feature selection method for high-dimensional biomedical data based on the ...
Yongqiang Dai, Lili Niu, Linjing Wei, Jie Tang   +3 more
doaj   +1 more source

Missing Data Imputation with High-Dimensional Data

The American Statistician, 2023
Imputation of missing data in high-dimensional datasets with more variables P than samples N, P≫N, is hampered by the data dimensionality. For multivariate imputation, the covariance matrix is ill conditioned and cannot be properly estimated. For fully conditional imputation, the regression models for imputation cannot include all the variables.
Alberto Brini, Edwin R. van den Heuvel
openaire   +1 more source

Longitudinal study of infants born preterm (

BMJ Open
Purpose The SEV-IDF programme aims to track infants born before 33 weeks of gestation, with very low birth weight (VLBW), neonatal encephalopathy or severe birth anomalies and perinatal disease.
Michèle Granier, Véronique Zupan-Simunek, Pascale Tubert‐Bitter, Alexandre Lapillonne, Matthieu Hanf, Audrey Rolland, Cécile Lebeaux, Lucas Anzelin, Anne C M Thiébaut, Laurence Leloup, Sylvie Escolano, Laurence Desplanques, SEV-IDF study group, Laureline Mathieu, Manuela Briand, Michèle Manière, Simona Sermasi, Nadia Medejel   +17 more
doaj   +1 more source

An efficient predictive analytics system for high dimensional big data

Journal of King Saud University: Computer and Information Sciences, 2022
The excessive growth of high dimensional big data has resulted in a greater challenge for data scientists to efficiently obtain valuable knowledge from these data. Traditional data mining techniques are not fit to process big data.
Myat Cho Mon Oo, Thandar Thein
doaj   +1 more source

Evidence‐Informed Multidisciplinary Consensus Guidance for the Psychosocial Care of Adolescents With High‐Risk Cancer: Recommendations From the Italian Association of Pediatric Hematology and Oncology

Pediatric Blood &Cancer, EarlyView.
ABSTRACT Background Adolescents with high‐risk cancer face complex developmental, psychosocial, and ethical challenges that extend beyond disease‐directed treatment. Although international recommendations exist for communication, psychosocial care, pediatric palliative care, survivorship, and shared decision‐making, these have largely evolved within ...
Johanna M. C. Blom, Dorella Scarponi, Giulia Zucchetti, Teresa Perillo, Maria Montanaro, Elisa Marconi, Chiara Rutigliano, Paolo Colavero, Paola Quarello, Marina Bertolotti, Monica Cellini, Carlo Clerici, Maurizio Mascarin, Francesca Bomben, Andrea Ferrari, the expert workgroup   +15 more
wiley   +1 more source

Comparative Drug Response Profiling in Neuroblastoma Cell Lines and Patient‐Derived Tumor Organoids

Pediatric Blood &Cancer, EarlyView.
ABSTRACT High‐risk neuroblastoma remains a leading cause of pediatric cancer mortality, and improved preclinical models are needed to guide therapeutic developments. We screened seven high‐risk neuroblastoma cell lines and three patient‐derived tumor organoids with 528 compounds alongside bone marrow controls, and compared them with external datasets ...
Krzysztof Wierbiłowicz, Se Whee Sammy Park, Aironas Los, Ernest Liu, Nona Struyf, Tom Erkers, Sören Lehmann, Jan J. Molenaar, Jakob Stenman, Olli Kallioniemi, Päivi Östling, Brinton Seashore‐Ludlow, Kasper Karlsson   +12 more
wiley   +1 more source

Clustering Evaluation in High-Dimensional Data [PDF]

, 2016
Clustering evaluation plays an important role in unsupervised learning systems, as it is often necessary to automatically quantify the quality of generated cluster configurations. This is especially useful for comparing the performance of different clustering algorithms as well as determining the optimal number of clusters in clustering algorithms that
Nenad Tomašev, Miloš Radovanović
openaire   +2 more sources

Domain Adaptation Principal Component Analysis: Base Linear Method for Learning with Out-of-Distribution Data

Entropy, 2022
Domain adaptation is a popular paradigm in modern machine learning which aims at tackling the problem of divergence (or shift) between the labeled training and validation datasets (source domain) and a potentially large unlabeled dataset (target domain).
Evgeny M. Mirkes, Jonathan Bac, Aziz Fouché, Sergey V. Stasenko, Andrei Zinovyev, Alexander N. Gorban   +5 more
doaj   +1 more source