Min Zhu

Work place: School of Information Science and Technology, East China Normal University, Shanghai 200062, China

E-mail: mzhu@cc.ecnu.edu.cn

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Biography

Author Articles
Effective Training Data Improved Ensemble Approaches for Urinalysis Model

By Ping Wu Min Zhu Peng Pu Tang Jiang

DOI: https://doi.org/10.5815/ijmecs.2011.04.04, Pub. Date: 8 Aug. 2011

Urinalysis remains one of the most commonly performed tests in clinical practice. Laboratory work can be greatly relieved by automated analyzing techniques. However, noisy and imbalanced urine samples make automatically identifying and classifying urine-related diseases become very difficult. This paper proposed hybrid sampling-based ensemble learning strategies by improving training data and classification performance. Having compared the effectiveness of several learning classifiers and data processing techniques, the experiments showed that the suggesting methods provided better classification accuracy than other approaches.

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