High-dimensional data often contain noisy and redundant features, posing challenges for accurate and efficient feature selection. To address this, a dynamic multitask learning framework is proposed, ...
Background: The immunosuppressant tacrolimus (TAC) plays a crucial role in preventing rejection reactions after organ transplant. Due to a narrow therapeutic window, it is one of the long-term ...
This study explores the development of two predictive models for the yield sooting index (YSI) of various fuels using the advanced capabilities of machine learning (ML), particularly multilayer ...
Abstract: In text classification, feature selection is essential to improve the classification effectiveness. This paper provides an empirical study of a feature selection method based on genetic ...
Cancer machine learning research is often limited by overparameterization and overfitting, which arise because cancer ‘omic’ variables significantly outnumber patient samples. Traditional feature ...
Adaptive Lasso is an extension of the standard Lasso method that provides improved feature selection properties through weighted L1 penalties. It assigns different weights to different coefficients in ...
The race for more data is dominating the wellness industry. More people are tracking their sleep, monitoring their glucose levels, and analyzing their step count as a way to optimize, or even gamify, ...
Abstract: When performing feature selection on most data sets, there is a general situation that some different feature subsets have the same number of selected ...
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