Datasets have relevant and irrelevant features whose evaluations are fundamental for classification or clustering processes. The effects of these relevant features make classification accuracy more accurate and stable. At this point, optimization methods are used for feature selection process. This process is a feature reduction process finding the most relevant feature subset without decrement of the accuracy rate obtained by original feature sets. Varied nature inspiration-based optimization algorithms have been proposed as feature selector. The density of data in construction projects and the inability of extracting these data cause various losses in field studies. In this respect, the behaviors of leaders are important in the selection and efficient use of these data. The objective of this study is implementing Artificial Bee Colony (ABC) algorithm as a feature selection method to predict the leadership perception of the construction employees. When Random Forest, Sequential Minimal Optimization and K-Nearest Neighborhood (KNN) are used as classifier, 84.1584% as highest accuracy result and 0.805 as highest F-Measure result were obtained by using KNN and Random Forest classifier with proposed ABC Algorithm as feature selector. The results show that a nature inspiration-based optimization algorithm like ABC algorithm as feature selector is satisfactory in prediction of the Construction Employee’s Leadership Perception.

中文翻译：

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拟议的人工蜂群算法作为特征选择器来预测站点管理者的领导力感知

数据集具有相关和不相关的特征，其评估对于分类或聚类过程至关重要。这些相关功能的作用使分类准确性更加准确和稳定。此时，将优化方法用于特征选择过程。此过程是特征缩减过程，用于找到最相关的特征子集，而不会降低原始特征集获得的准确率。已经提出了多种多样的基于自然灵感的优化算法作为特征选择器。建设项目中数据的密度以及无法提取这些数据会在现场研究中造成各种损失。在这方面，领导者的行为对于选择和有效利用这些数据很重要。这项研究的目的是将人工蜂群（ABC）算法作为一种特征选择方法来预测建筑工人的领导力感知。当使用随机森林，顺序最小优化和K最近邻（KNN）作为分类器时，使用KNN和随机森林分类器并以拟议的ABC算法作为特征选择器，可以得到84.1584％的最高准确度结果和0.805的最高F-Measure结果。 。结果表明，基于自然灵感的优化算法（如ABC算法作为特征选择器）在预测建筑工人的领导力感知方面令人满意。序列最小优化和K最近邻（KNN）被用作分类器，通过使用KNN和随机森林分类器并以拟议的ABC算法作为特征选择器，获得了84.1584％的最高准确度结果和0.805的最高F测度结果。结果表明，基于自然灵感的优化算法（如ABC算法作为特征选择器）在预测建筑工人的领导力感知方面令人满意。序列最小优化和K最近邻（KNN）被用作分类器，通过使用KNN和随机森林分类器并以拟议的ABC算法作为特征选择器，获得了84.1584％的最高准确度结果和0.805的最高F测度结果。结果表明，基于自然灵感的优化算法（如ABC算法作为特征选择器）在预测建筑工人的领导力感知方面令人满意。