Aug 31, 2023 · This paper presents a new methodology based on metaheuristics to dynamically select features. This approach has been applied to train a deep neural network.
Feature selection as a building block in ML is crucial because it directly impacts the performance and predictive power of a model by selecting the most ...
Feature Selection Guided by CVOA Metaheuristic for Deep Neural Networks: Application to Multivariate Time Series Forecasting. https://doi.org/10.1007/978-3 ...
Feature Selection Guided by CVOA Metaheuristic for Deep Neural Networks: Application to Multivariate Time Series Forecasting. Jiménez-Navarro, M.J.; Restrepo ...
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Feature Selection Guided by CVOA Metaheuristic for Deep Neural Networks: Application to Multivariate Time Series Forecasting. 2023, Lecture Notes in Networks ...
Feature selection guided by CVOA metaheuristic for deep neural networks: Application to multivariate time series forecasting. MJ Jiménez-Navarro, C Restrepo ...
May 21, 2024 · This paper introduces a novel filter feature selection method that integrates the Conditional Dependence Coefficient metric with an evolutionary algorithm to ...
Feature selection (FS), sometimes called variable selection, is an important preprocessing step for several data mining applications.
Sep 26, 2024 · This paper presents a new feature selection (FS) algorithm based on the wrapper approach using neural networks (NNs).
Oct 11, 2022 · This paper provides a comprehensive review of the optimization of ANNs and DLs using MH algorithms.