Hybrid Gradient Descent Grey Wolf Optimizer for Optimal Feature Selection

Abstract

Feature selection is the process of decreasing the number of features in a dataset by removing redundant, irrelevant, and randomly class-corrected data features. By applying feature selection on large and highly dimensional datasets, the redundant features are removed, reducing the complexity of the data and reducing training time. The objective of this paper was to design an optimizer that combines the well-known metaheuristic population-based optimizer, the grey wolf algorithm, and the gradient descent algorithm and test it for applications in feature selection problems. The proposed algorithm was first compared against the original grey wolf algorithm in 23 continuous test functions. The proposed optimizer was altered for feature selection, and 3 binary implementations were developed with final implementation compared against the two implementations of the binary grey wolf optimizer and binary grey wolf particle swarm optimizer on 6 medical datasets from the UCI machine learning repository, on metrics such as accuracy, size of feature subsets, F -measure, accuracy, precision, and sensitivity. The proposed optimizer outperformed the three other optimizers in 3 of the 6 datasets in average metrics. The proposed optimizer showed promise in its capability to balance the two objectives in feature selection and could be further enhanced.

Multiclass feature selection with metaheuristic optimization algorithms: a review

“…A neural network (NN) based on GWO was utilized to categorize the various emotions from the selected features. Kitonyi and Segera [ 133 ] presented a hybridization of a popular metaheuristic optimizer called GWO and gradient descent algorithm to resolve feature selection issues. They first compared the approach with the baseline GWO in twenty-three test functions and developed three binary implementations, and compared the final implementation against two implementations of the binary GWO and binary GWPSO using six medical datasets taken from the UCI repository on the rate of accuracy, the number of features selected subsets, precision, F-measure, and sensitivity metrics.…”

A Hyperparameter Optimization for Galaxy Classification

“…The Grey Wolf Optimizer (GWO) algorithm is used to obtain the optimum CNN architecture to achieve the best classification result. The GWO algorithm is used in solving optimization problems [20][21][22][23] and improving other metaheuristic optimization algorithms [24][25][26][27][28][29]. It is preferred for hyperparameter optimization of the CNN architecture.…”

Binary dwarf mongoose optimizer for solving high-dimensional feature selection problems

“…The authors in [ 46 ] presented a hybridization of a popular metaheuristic optimizer called GWO and a gradient descent algorithm which was used to resolve feature selection issues. Similarly, a newly proposed hybridized technique comprised the Extended Binary Cuckoo Search, Genetic Algorithm, and Whale Optimization Algorithm, which aimed to reduce the time required to search a huge database during image retrieval.…”