ABC-SVM: Artificial Bee Colony Optimization for Feature Selection in Medical Diagnosis: A Comprehensive Literature Survey
Abstract
Feature selection is a critical preprocessing step in machine learning, particularly for medical diagnosis, where high-dimensional datasets often contain irrelevant or redundant features that degrade classifier performance. The Artificial Bee Colony algorithm, inspired by the intelligent foraging behavior of honey bees, has emerged as a powerful metaheuristic for solving complex feature selection problems. This literature survey provides a comprehensive examination of Artificial Bee Colony-based feature selection frameworks integrated with Support Vector Machines for medical diagnosis. The paper reviews the theoretical foundations of the Artificial Bee Colony algorithm, its biological inspiration, and operational phases involving employed, onlooker, and scout bees. Various representation schemes for feature selection, including binary encoding and multi-objective fitness functions, are analyzed. The survey critically evaluates empirical results from comparative studies against Genetic Algorithms and Particle Swarm Optimization across multiple medical datasets. Key findings reveal that the Artificial Bee Colony with Support Vector Machine achieves substantial feature reduction, ranging from 40% to 64%, while maintaining classification accuracy between 88% and 98%. Research gaps are identified, including the need for adaptive parameter control, handling of high-dimensional data, and integration with deep learning architectures. The survey concludes that Artificial Bee Colony with Support Vector Machine represents a promising direction for developing parsimonious, accurate, and interpretable medical diagnosis systems.
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