International Journal of Modern Education and Computer Science (IJMECS)
IJMECS Vol. 3, No. 4, 8 Aug. 2011
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Effective Training Data Improved Ensemble Approaches for Urinalysis Model
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Author(s)
Ping Wu
1,*
Min Zhu
1
Peng Pu
1
Tang Jiang
2
Index Terms
Urinalysis, noisy data, imbalanced data, sampling methods, classification, ensembles
Abstract
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.
Cite This Paper
Ping Wu, Min Zhu, Peng Pu, Tang Jiang, "Effective Training Data Improved Ensemble Approaches for Urinalysis Model", International Journal of Modern Education and Computer Science(IJMECS), vol.3, no.4, pp.25-31, 2011. DOI:10.5815/ijmecs.2011.04.04
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