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Dujuan Wang
Researcher at Sichuan University
Publications - 69
Citations - 1532
Dujuan Wang is an academic researcher from Sichuan University. The author has contributed to research in topics: Computer science & Scheduling (computing). The author has an hindex of 19, co-authored 48 publications receiving 881 citations. Previous affiliations of Dujuan Wang include Dalian Maritime University & University of Science and Technology of China.
Papers
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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 multi-objective evolutionary algorithm guided by directed search for dynamic scheduling
Dujuan Wang,Feng Liu,Yaochu Jin +2 more
TL;DR: Experimental results demonstrate that the proposed directed search strategy (DSS) is effective in handling the dynamic scheduling problems under investigation, under the assumption that jobs can be rejected and job processing time is controllable.
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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.