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Yanzhang Wang
Researcher at Dalian University of Technology
Publications - 47
Citations - 725
Yanzhang Wang is an academic researcher from Dalian University of Technology. The author has contributed to research in topics: Ensemble learning & Decision support system. The author has an hindex of 11, co-authored 46 publications receiving 376 citations. Previous affiliations of Yanzhang Wang include Institute for Infocomm Research Singapore.
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Stacking-based ensemble learning of decision trees for interpretable prostate cancer detection
TL;DR: A stacking-based ensemble learning method is proposed that simultaneously constructs the diagnostic model and extracts interpretable diagnostic rules from the constructed ensemble learning model, which outperforms that of several state-of-the-art methods in terms of the classification accuracy, specificity and specificity.
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An improved support vector machine-based diabetic readmission prediction.
TL;DR: A novel method combining support vector machine and genetic algorithm to build the risk prediction model, which simultaneously involves feature selection and the processing of imbalanced data is presented, which outperforms other popular algorithms in identifying diabetic patients who may be readmitted.
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An improved random forest-based rule extraction method for breast cancer diagnosis
TL;DR: Improved Random Forest-based rule extraction (IRFRE) method is developed to derive accurate and interpretable classification rules from a decision tree ensemble for breast cancer diagnosis and can be popularized to other cancer diagnoses in practice, which provides an option to a more interpretable, more accurate cancer diagnosis process.
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A stacking-based ensemble learning method for earthquake casualty prediction
TL;DR: It was found that the stacking ensemble learning method can effectively integrate the prediction results of the base learner to improve the performance of the model, and the improved swarm intelligence algorithm can further improve the prediction accuracy.
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Multi-view ensemble learning based on distance-to-model and adaptive clustering for imbalanced credit risk assessment in P2P lending
TL;DR: Experimental results demonstrate the superiority of the proposed DM–ACME learning method as well as indicate the importance of some features in loan default prediction.