Phenomics and robust multiomics data for cardiovascular disease subty**
E Maiorino, J Loscalzo - Arteriosclerosis, Thrombosis, and …, 2023 - Am Heart Assoc
The complex landscape of cardiovascular diseases encompasses a wide range of related
pathologies arising from diverse molecular mechanisms and exhibiting heterogeneous …
pathologies arising from diverse molecular mechanisms and exhibiting heterogeneous …
[HTML][HTML] A framework for feature selection through boosting
As dimensions of datasets in predictive modelling continue to grow, feature selection
becomes increasingly practical. Datasets with complex feature interactions and high levels …
becomes increasingly practical. Datasets with complex feature interactions and high levels …
Feature-specific mutual information variation for multi-label feature selection
Recent years has witnessed urgent needs for addressing the curse of dimensionality
regarding multi-label data, which attracts wide attention for feature selection. Feature …
regarding multi-label data, which attracts wide attention for feature selection. Feature …
Feature selection in image analysis: a survey
V Bolon-Canedo, B Remeseiro - Artificial Intelligence Review, 2020 - Springer
Image analysis is a prolific field of research which has been broadly studied in the last
decades, successfully applied to a great number of disciplines. Since the apparition of Big …
decades, successfully applied to a great number of disciplines. Since the apparition of Big …
Manifold learning with structured subspace for multi-label feature selection
Nowadays, multi-label learning is ubiquitous in practical applications, in which multi-label
data is always confronted with the curse of high-dimensional features. Feature selection has …
data is always confronted with the curse of high-dimensional features. Feature selection has …
A novel hybrid feature selection method based on dynamic feature importance
G Wei, J Zhao, Y Feng, A He, J Yu - Applied Soft Computing, 2020 - Elsevier
Feature selection aims to eliminate unimportant and redundant features or to select effective
and interacting features. It is a challenging task to accurately measure the relationships of …
and interacting features. It is a challenging task to accurately measure the relationships of …
Hybrid method to supervise feature selection using signal processing and complex algebra techniques
Research in AI has proved to be revolutionarily beneficial to humankind from the past few
decades. Many supporting techniques have been developed that indirectly evolved AI and …
decades. Many supporting techniques have been developed that indirectly evolved AI and …
Distinguishing two types of labels for multi-label feature selection
Multi-label feature selection plays an important role in pattern recognition, which can
improve multi-label classification performance. In traditional multi-label feature selection …
improve multi-label classification performance. In traditional multi-label feature selection …
Identifying cancer targets based on machine learning methods via Chou's 5-steps rule and general pseudo components
R Liang, J **e, C Zhang, M Zhang… - Current topics in …, 2019 - ingentaconnect.com
In recent years, the successful implementation of human genome project has made people
realize that genetic, environmental and lifestyle factors should be combined together to …
realize that genetic, environmental and lifestyle factors should be combined together to …
Non-negative multi-label feature selection with dynamic graph constraints
Y Zhang, Y Ma - Knowledge-Based Systems, 2022 - Elsevier
Feature selection can combat dimension disasters and improve the performance of
classification algorithms, so multi-label feature selection is an essential part of multi-label …
classification algorithms, so multi-label feature selection is an essential part of multi-label …