Robust algorithm to learn rules for classification: A fault diagnosis case study

Machine learning algorithms are used for building classifier models. The rule-based decision tree classifiers popular ones. However, the performance of varies with hyperparameter tuning. optimum values obtained using either optimization or trial and error methods. present study utilizes MODLEM algorithm to overcome drawbacks accounted by algorithms. Eliminating tuning producing results closer standard makes a robust classification algorithm. robustness is illustrated fault diagnosis case study. faults an automobile suspension system vibration signals acquired at various conditions.