Discretized butterfly optimization algorithm for variable selection in the rapid determination of cholesterol by near-infrared spectroscopy

Abstract

The discretized butterfly optimization algorithm is proposed as a variable selection tool combined with near-infrared spectroscopy for measuring the cholesterol concentration in blood samples.

Enhancing Self-Care Prediction in Children with Impairments: A Novel Framework for Addressing Imbalance and High Dimensionality

“…Additionally, hyperparameters may affect how effectively the method performs for advanced machine learning models. There are some primary automated hyperparameter determination techniques (HPO) such as grid search [31], random search [31], gradient-based optimization [31], grey wolf optimizer [32], whale optimization algorithm [33], etc. A grid search (GS) approach was used, and our goal is to devise classifiers for every conceivable combination of selected hyperparameter values, evaluate these classifiers, and select the optimal model based on the performance of the validation data.…”

A hybrid variable selection method combining Fisher's linear discriminant combined population analysis and an improved binary cuckoo search algorithm

“…These methods construct multiple models by using different subsets of variables and select the best combination of variables by comparing the performance of the models. In addition, variable selection methods based on swarm intelligence optimization algorithms, such as the gray wolf optimization (GWO) algorithm, 21 whale optimization algorithm (WOA), 22 and butterfly optimization algorithm (BOA), 23 have likewise received wide attention. These methods select the best combination of variables by continuously and iteratively adjusting the combination of variable subsets to maximize or minimize the objective function ( e.g.…”

Quantitative analysis of spectral data based on stochastic configuration networks

“…15 According to this law, there is a linear relationship between the spectral intensity or absorbance and the content of substance components. Linear models are applied due to their fewer parameters and faster operation speed, such as multiple linear regression (MLR), 16 partial least squares (PLS), 17,18 principal component regression (PCR), 19 least absolute shrinkage and selection operator (Lasso), 18,20 Elastic Net 21 and so on. However, the Lambert–Beer Law is based on the assumption of monochromatic light and dilute solutions, and it does not consider the interactions between absorbing solute molecules and neighboring molecules.…”