An Adaptive Sampling Ensemble Learning Method for Urinalysis Model

Improvements in automated urinalysis are largely requested by laboratory practice. Urine samples with noise and imbalance increase the difficulty of identifying and classifying urine-related diseases. For improving classification performance, this paper compared the effectiveness of several learning classifiers and proposed a hybrid sampling-based ensemble learning method. The experiments show that our suggesting method provided better classification accuracy than other approaches.