Discovering causal relations and equations from data
Physics is a field of science that has traditionally used the scientific method to answer
questions about why natural phenomena occur and to make testable models that explain the …
questions about why natural phenomena occur and to make testable models that explain the …
Feature selection: A data perspective
Feature selection, as a data preprocessing strategy, has been proven to be effective and
efficient in preparing data (especially high-dimensional data) for various data-mining and …
efficient in preparing data (especially high-dimensional data) for various data-mining and …
[HTML][HTML] Benchmark for filter methods for feature selection in high-dimensional classification data
Feature selection is one of the most fundamental problems in machine learning and has
drawn increasing attention due to high-dimensional data sets emerging from different fields …
drawn increasing attention due to high-dimensional data sets emerging from different fields …
SNIB: improving spike-based machine learning using nonlinear information bottleneck
S Yang, B Chen - IEEE Transactions on Systems, Man, and …, 2023 - ieeexplore.ieee.org
Spiking neural networks (SNNs) have garnered increased attention in the field of artificial
general intelligence (AGI) research due to their low power consumption, high computational …
general intelligence (AGI) research due to their low power consumption, high computational …
Feature selection based on mutual information with correlation coefficient
H Zhou, X Wang, R Zhu - Applied intelligence, 2022 - Springer
Feature selection is an important preprocessing process in machine learning. It selects the
crucial features by removing irrelevant features or redundant features from the original …
crucial features by removing irrelevant features or redundant features from the original …
Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls
Neuroimaging-based single subject prediction of brain disorders has gained increasing
attention in recent years. Using a variety of neuroimaging modalities such as structural …
attention in recent years. Using a variety of neuroimaging modalities such as structural …
[HTML][HTML] Machine learning methods for quantitative radiomic biomarkers
Radiomics extracts and mines large number of medical imaging features quantifying tumor
phenotypic characteristics. Highly accurate and reliable machine-learning approaches can …
phenotypic characteristics. Highly accurate and reliable machine-learning approaches can …
Feature selection with multi-view data: A survey
This survey aims at providing a state-of-the-art overview of feature selection and fusion
strategies, which select and combine multi-view features effectively to accomplish …
strategies, which select and combine multi-view features effectively to accomplish …
A survey of crypto ransomware attack detection methodologies: an evolving outlook
A Alqahtani, FT Sheldon - Sensors, 2022 - mdpi.com
Recently, ransomware attacks have been among the major threats that target a wide range
of Internet and mobile users throughout the world, especially critical cyber physical systems …
of Internet and mobile users throughout the world, especially critical cyber physical systems …
Benchmark of filter methods for feature selection in high-dimensional gene expression survival data
A Bommert, T Welchowski, M Schmid… - Briefings in …, 2022 - academic.oup.com
Feature selection is crucial for the analysis of high-dimensional data, but benchmark studies
for data with a survival outcome are rare. We compare 14 filter methods for feature selection …
for data with a survival outcome are rare. We compare 14 filter methods for feature selection …