An improved mountain gazelle optimizer based on chaotic map and spiral disturbance for medical feature selection
Ying Li, Yanyu Geng, Huankun Sheng
TL;DR
This paper introduces an improved mountain gazelle optimizer algorithm for better medical feature selection, showing it outperforms existing methods.
Contribution
A novel binary mountain gazelle optimizer with chaotic initialization and spiral disturbance for medical feature selection.
Findings
BIMGO outperforms 8 metaheuristic algorithms on 16 medical datasets in terms of fitness and feature selection.
The algorithm shows superior convergence and sensitivity in selecting effective medical features.
IMGO demonstrates strong performance on 23 benchmark datasets for continuous problems.
Abstract
Feature selection is an important solution for dealing with high-dimensional data in the fields of machine learning and data mining. In this paper, we present an improved mountain gazelle optimizer (IMGO) based on the newly proposed mountain gazelle optimizer (MGO) and design a binary version of IMGO (BIMGO) to solve the feature selection problem for medical data. First, the gazelle population is initialized using iterative chaotic map with infinite collapses (ICMIC) mapping, which increases the diversity of the population. Second, a nonlinear control factor is introduced to balance the exploration and exploitation components of the algorithm. Individuals in the population are perturbed using a spiral perturbation mechanism to enhance the local search capability of the algorithm. Finally, a neighborhood search strategy is used for the optimal individuals to enhance the exploitation and…
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Taxonomy
TopicsMetaheuristic Optimization Algorithms Research · Artificial Intelligence in Healthcare · Advanced Multi-Objective Optimization Algorithms