As a nature-inspired algorithm, artificial bee colony (ABC) is an optimization algorithm that is inspired by the search behaviour of honey bees. The main aim of this study is to examine the effects of the ABC-based feature selection algorithm on classification performance for cyberbullying, which has become a significant worldwide social issue in recent years. With this purpose, the classification performance of the proposed ABC-based feature selection method is compared with three different traditional methods such as information gain, ReliefF and chi square. Experimental results present that ABC-based feature selection method outperforms than three traditional methods for the detection of cyberbullying. The Macro averaged F_measure of the data set is increased from 0.659 to 0.8 using proposed ABC-based feature selection method.

中文翻译：

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基于人工蜂群的网络欺凌特征选择算法

作为自然启发算法，人工蜂群（ABC）是一种优化算法，其灵感来自蜜蜂的搜索行为。这项研究的主要目的是研究基于ABC的特征选择算法对网络欺凌分类性能的影响，近年来，这已成为世界范围内的重要社会问题。为此，将所提出的基于ABC的特征选择方法的分类性能与三种不同的传统方法（如信息增益，ReliefF和卡方）进行比较。实验结果表明，基于ABC的特征选择方法优于三种传统的网络欺凌检测方法。使用建议的基于ABC的特征选择方法，数据集的宏平均F_measure从0.659增加到0.8。