[HTML][HTML] Dual regularized unsupervised feature selection based on matrix factorization and minimum redundancy with application in gene selection
Gene expression data have become increasingly important in machine learning and
computational biology over the past few years. In the field of gene expression analysis …
computational biology over the past few years. In the field of gene expression analysis …
Feature selection with missing labels using multilabel fuzzy neighborhood rough sets and maximum relevance minimum redundancy
Recently, multilabel classification has generated considerable research interest. However,
the high dimensionality of multilabel data incurs high costs; moreover, in many real …
the high dimensionality of multilabel data incurs high costs; moreover, in many real …
Temperature-induced deflection separation based on bridge deflection data using the TVFEMD-PE-KLD method
S Li, J **n, Y Jiang, C Wang, J Zhou, X Yang - Journal of Civil Structural …, 2023 - Springer
The bridge deflection data measured in field are greatly affected by temperature. In some
situations, temperature can be the dominant factor comparing with loads and other factors …
situations, temperature can be the dominant factor comparing with loads and other factors …
Multi-strategy ensemble binary hunger games search for feature selection
BJ Ma, S Liu, AA Heidari - Knowledge-Based Systems, 2022 - Elsevier
Feature selection is a crucial preprocessing step in the sphere of machine learning and data
mining, devoted to reducing the data dimensionality to improve the performance of learning …
mining, devoted to reducing the data dimensionality to improve the performance of learning …
A hybrid transfer learning scheme for remaining useful life prediction and cycle life test optimization of different formulation Li-ion power batteries
Long-term cycle life test in battery development is crucial for formulations selection but time-
consuming and high-cost. To shorten cycle test with estimated lifespan, a prediction-based …
consuming and high-cost. To shorten cycle test with estimated lifespan, a prediction-based …
A novel relation aware wrapper method for feature selection
Feature selection, aiming at eliminating irrelevant and redundant features, is an important
data preprocessing technology for downstream tasks, eg, classification. With the explosive …
data preprocessing technology for downstream tasks, eg, classification. With the explosive …
Feature clustering-Assisted feature selection with differential evolution
Modern data collection technologies may produce thousands of or even more features in a
single dataset. The high dimensionality of data poses a barrier to determining discriminating …
single dataset. The high dimensionality of data poses a barrier to determining discriminating …
Stable feature selection using copula based mutual information
Feature selection is a key step in many machine learning tasks. A majority of the existing
methods of feature selection address the problem by devising some scoring function while …
methods of feature selection address the problem by devising some scoring function while …
Time pattern reconstruction for classification of irregularly sampled time series
Abstract Irregularly Sampled Time Series (ISTS) include partially observed feature vectors
caused by the lack of temporal alignment across dimensions and the presence of variable …
caused by the lack of temporal alignment across dimensions and the presence of variable …
Multivariate time-series classification of critical events from industrial drying hopper operations: A deep learning approach
In recent years, the advancement of Industry 4.0 and smart manufacturing has made a large
amount of industrial process data attainable with the use of sensors installed on machines …
amount of industrial process data attainable with the use of sensors installed on machines …