Learning to Fairly Classify the Quality of Wireless Links
This work addresses fairness in wireless link quality classification, which is important for network reliability, but it is incremental as it builds on existing methods with specific enhancements.
The paper tackled the problem of developing accurate and fair link quality classifiers for wireless networks on imbalanced datasets, showing that a proposed tree-based model achieved the best trade-off in performance, fairness, and training time, with improvements of over 40% in minority class performance through feature selection and over 20% through resampling.
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.