The Horse Herd Optimization Algorithm (HOA) is a new meta-heuristic algorithm
based on the behaviors of horses at different ages. The HOA was introduced
recently to solve complex and high-dimensional problems. This paper proposes a
binary version of the Horse Herd Optimization Algorithm (BHOA) in order to
solve discrete problems and select prominent feature subsets. Moreover, this
study provides a novel hybrid feature selection framework based on the BHOA and
a minimum Redundancy Maximum Relevance (MRMR) filter method. This hybrid
feature selection, which is more computationally efficient, produces a
beneficial subset of relevant and informative features. Since feature selection
is a binary problem, we have applied a new Transfer Function (TF), called
X-shape TF, which transforms continuous problems into binary search spaces.
Furthermore, the Support Vector Machine (SVM) is utilized to examine the
efficiency of the proposed method on ten microarray datasets, namely Lymphoma,
Prostate, Brain-1, DLBCL, SRBCT, Leukemia, Ovarian, Colon, Lung, and MLL. In
comparison to other state-of-the-art, such as the Gray Wolf (GW), Particle
Swarm Optimization (PSO), and Genetic Algorithm (GA), the proposed hybrid
method (MRMR-BHOA) demonstrates superior performance in terms of accuracy and
minimum selected features. Also, experimental results prove that the X-Shaped
BHOA approach outperforms others methods.

该研究提出了一种二进制版本的马群优化算法 (BHOA)，并基于 BHOA 和最小冗余最大相关过滤器方法提出了一种新颖的混合特征选择框架。实验结果表明，X 形状 BHOA 方法在准确性和选取最少特征方面优于其他方法。