High‐Dimensional Feature Selection Based on Improved Binary Ant Colony Optimization Combined with Hybrid Rice Optimization Algorithm

Abstract

In the realm of high-dimensional data analysis, numerous fields stand to benefit from its applications, including the biological and medical sectors that are crucial for computer-aided disease diagnosis and prediction systems. However, the presence of a significant number of redundant or irrelevant features can adversely affect system accuracy and real-time diagnosis efficiency. To mitigate this issue, this paper proposes two innovative wrapper feature selection (FS) methods that integrate the ant colony optimization (ACO) algorithm and hybrid rice optimization (HRO). HRO is a recently developed metaheuristic that mimics the breeding process of the three-line hybrid rice, which is yet to be thoroughly explored in the context of solving high-dimensional FS problems. In the first hybridization, ACO is embedded as an evolutionary operator within HRO and updated alternately with it. In the second form of hybridization, two subpopulations evolve independently, sharing the local search results to assist individual updating. In the initial stage preceding hybridization, a problem-oriented heuristic factor assignment strategy based on the importance of the knee point feature is introduced to enhance the global search capability of ACO in identifying the smallest and most representative features. The performance of the proposed algorithms is evaluated on fourteen high-dimensional biomedical datasets and compared with other recently advanced FS methods. Experimental results suggest that the proposed methods are efficient and computationally robust, exhibiting superior performance compared to the other algorithms involved in this study.

Hybrid rice optimization algorithm inspired grey wolf optimizer for high-dimensional feature selection

“…Its good performance in solving 0-1 knapsack problem 40 , band selection problem 41 , computeraided diagnosis 42 , and intrusion detection 43 demonstrates its notable search efficiency and robust global search capabilities. Furthermore, the concept of hybridization combined with metaheuristic algorithms has been successfully applied to FS 44 .…”

“…A fair comparison is conducted with eight state-of-the-art approaches mentioned in the previous section and three methods based on variant HRO including modified HRO (MHRO) 41 , improved binary ACO melded with HRO in the relay model (R-IBACO) and the collaborative model (C-IBACO) 44 . In the evaluation of high-dimensional biomedical datasets through forty experiments, the framework based on HRO-GWO emerges as the top performer in the fitness value and accuracy comparison on all forty occasions.…”

Combination prediction method of students’ performance based on ant colony algorithm

“…Zhang [40] improved the decision tree classification method and ant colony algorithm, and established a data mining model for student employment and entrepreneurship. Ye et al [41] proposed two innovative wrapper feature selection methods by integrating the ant colony optimization algorithm and hybrid rice optimization. Zhao [42] proposed a value prediction and analysis method of network documents based on ant colony algorithm.…”