Results 31 to 40 of about 1,150,244 (261)
Frontiers in Cardiovascular Medicine, 2022 Growing evidence suggests a wide spectrum of potential cardiovascular complications following cancer therapies, leading to an urgent need for better risk-stratifying and disease screening in patients undergoing oncological treatment.
Haidee Chen +5 more
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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 +3 more
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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 +5 more
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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 +3 more
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Recession forecasting with high‐dimensional data [PDF]
Journal of Forecasting, 2021 AbstractIn this paper, a large amount of different financial and macroeconomic variables are used to predict the U.S. recession periods. We propose a new cost‐sensitive extension to the gradient boosting model, which can take into account the class imbalance problem of the binary response variable.
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Longitudinal study of infants born preterm (
BMJ OpenPurpose 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 +17 more
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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
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Outlier Detection in High Dimensional Data [PDF]
Journal of Information & Knowledge Management, 2020 High-dimensional data poses unique challenges in outlier detection process. Most of the existing algorithms fail to properly address the issues stemming from a large number of features. In particular, outlier detection algorithms perform poorly on dataset of small size with a large number of features.
Firuz Kamalov, Ho Hon Leung
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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
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Pediatric Blood &Cancer, EarlyView.ABSTRACT Background Adolescents with haematological malignancies face significant emotional and relational challenges, often accompanied by difficulties in communicating their needs within the healthcare context. To address these issues, a narrative‐based psycho‐educational intervention based on the creation and prescription of Ironic Medications was ...
Marta Stoppa +7 more
wiley +1 more source