The application of bio-inspired heuristics has led to improved performance in the case of various machine learning algorithms. The authors of [8] used a Particle Swarm Optimization (PSO) [9] Random Forest (RF) approach for the accurate diagnosis of spontaneous ruptures of ovarian endometriomas (OE). Compared to all other models, the PSO-RF approach led to the most accurate results. The authors of [10] improved the accuracy of the Naive Bayes (NB) algorithm for hoax classification using PSO, while the authors of [11] used an integrated approach of NB and PSO for email spam detection. In [12], an improved weighted K-Nearest Neighbors (K-NN) algorithm which was based on PSO was applied in wind power system state recognition. PSO was applied in that approach in the optimization of the weight and the k parameter. The authors of [13] applied the Artificial Flora Algorithm (AFA) [14] for feature selection and Gradient Boosted Trees (GBT) for classification of diabetes data. Logistic Regression (LR) and PSO were applied in [15] in the optimization of the reliability of a bank.  
Bio-inspired algorithms were applied in various types of machine learning optimization problems, ranging from the optimization of the sigma, C, and epsilon parameters of a Support Vector Regression (SVR) algorithm [16] and feature selection in classification problems [17], to tuning machine learning ensembles weights [18] and the selection of cluster centers in clustering problems [19].  
Interest in the application of swarm intelligence algorithms to feature selection increased a great deal in the last decades, as can be seen in [20], where the review results showed that the number of articles searched in Google Scholar hitting the terms \"swarm intelligence\" and \"feature selection\" per year increased significantly in recent years.  
The application of bio-inspired algorithms in feature selection for classification problems using machine learning therefore represents a good alternative to other methods considered in the research literature. A few state-of-the-art approaches for feature selection which do not involve bio-inspired algorithms are fuzzy entropy measures [21], feature distribution measures [22], and recursive feature elimination [23].  
The principal contributions of this article are:  
1. Presentation of the main steps of the original version of the Horse Optimization Algorithm (HOA) introduced in [24];
2. Adaptation of the HOA to the Binary Horse Optimization Algorithm (BHOA) for feature selection optimization problems;
3. Application of the BHOA to nine state-of-the-art datasets from the UCI machine learning repository [25], which are representative for classification problems, using the classification algorithms RF, SVM, GBT, LR, K-NN, and NB;
4. Comparison of the results with the ones obtained using three other bio-inspired approaches, based on binary versions of the GWO, PSO, and Crow Search Algorithm (CSA) [26].  
The structure of this article is as follows: Section 2 presents related work and is organized in two parts; the first introduces the HOA [24] and presents its applications, while the second presents related work in the context of bio-inspired approaches for feature selection. Section 3 presents the methods and materials and is organized in three parts; the first describes the high-level view of the HOA, the second shows how the HOA is adapted to the BHOA for feature selection, and the third presents the BHOA machine learning methodology for feature selection. Section 4 shows the results obtained using the BHOA-based methodology. Section 5 discusses the results, comparing them to the ones obtained using three other bio-inspired approaches based on the GWO, PSO, and CSA. Finally, Section 6 presents the conclusions.