Machine learning (ML) has been used to develop increasingly accurate link quality estimators for wireless networks. However, more in-depth questions regarding the most suitable class of models, most suitable metrics and model performance on imbalanced datasets remain open. In this paper, we propose a new tree-based link quality classifier that meets high performance and fairly classifies the minority class and, at the same time, incurs low training cost. We compare the tree-based model, to a multilayer perceptron (MLP) non-linear model and two linear models, namely logistic regression (LR) and SVM, on a selected imbalanced dataset and evaluate their results using five different performance metrics. Our study shows that 1) non-linear models perform slightly better than linear models in general, 2) the proposed non-linear tree-based model yields the best performance trade-off considering F1, training time and fairness, 3) single metric aggregated evaluations based only on accuracy can hide poor, unfair performance especially on minority classes, and 4) it is possible to improve the performance on minority classes, by over 40% through feature selection and by over 20% through resampling, therefore leading to fairer classification results.

中文翻译：

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学习对无线链路的质量进行公平分类

机器学习（ML）已用于为无线网络开发越来越精确的链路质量估计器。但是，关于最合适的模型类别，最合适的度量标准和不平衡数据集上模型性能的更深入的问题仍然存在。在本文中，我们提出了一种新的基于树的链路质量分类器，该分类器可以满足高性能并公平地对少数类进行分类，同时又降低了培训成本。我们在选定的不平衡数据集上将基于树的模型与多层感知器（MLP）非线性模型和两个线性模型（即逻辑回归（LR）和SVM）进行比较，并使用五个不同的性能指标评估其结果。我们的研究表明：1）非线性模型的总体效果略好于线性模型，