Players in the telecommunications sector struggle against the competition to keep customers, and therefore they need effective churn management. Most classification algorithms either ignore misclassification cost or assume that the costs of all incorrect classification errors are equal. But as in real life, many classification problems have different misclassification costs and this difference cannot be ignored. For this reason, studies on cost‐sensitive classification approaches have gained importance in recent years. The characteristics of telecommunications datasets such as high dimensionality and imbalance are making it difficult to achieve the desired performance for churn prediction. By taking this into consideration, we propose a multiobjective–cost‐sensitive ant colony optimization (MOC‐ACO‐Miner) approach which integrates the cost‐based nondominated sorted genetic algorithm feature selection and multiobjective ACO based cost‐sensitive learning. MOC‐ACO‐Miner is applied to one of Turkey's top 100 information technology companies for customer churn‐prediction. Finally, experiments find out that the model performs quite well with the area under receiver operating characteristic curve values of 0.9998 for predicting churners and therefore it can be beneficial for the highly competitive telecommunications sector.

中文翻译：

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土耳其电信行业的客户流失预测：拟议的多目标，成本敏感型蚁群优化算法

电信行业的竞争者与竞争对手竞争以保持客户，因此他们需要有效的客户流失管理。大多数分类算法要么忽略分类错误成本，要么假设所有错误分类错误的成本都相等。但是，就像在现实生活中一样，许多分类问题具有不同的错误分类成本，并且这种差异不能忽略。因此，近年来，对成本敏感的分类方法的研究变得越来越重要。电信数据集的特性（例如高维和不平衡）使得难以实现所需的流失预测性能。考虑到这一点，我们提出了一种多目标，成本敏感的蚁群优化（MOC-ACO-Miner）方法，该方法将基于成本的非优势排序遗传算法特征选择与基于多目标ACO的成本敏感学习进行了集成。MOC-ACO-Miner被应用于土耳其的前100名信息技术公司之一，以进行客户流失预测。最后，实验发现，该模型在接收机工作特性曲线值低于0.9998的区域内预测流失时表现良好，因此对于竞争激烈的电信行业可能是有益的。