An Adaptive Sampling Ensemble Learning Method for Urinalysis Model
Ping Wu, Min Zhu, Peng Pu, Tang Jiang · 2010
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.