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Yunqiang Yin
Researcher at University of Electronic Science and Technology of China
Publications - 53
Citations - 1585
Yunqiang Yin is an academic researcher from University of Electronic Science and Technology of China. The author has contributed to research in topics: Job shop scheduling & Scheduling (computing). The author has an hindex of 21, co-authored 53 publications receiving 1085 citations. Previous affiliations of Yunqiang Yin include Zhejiang University & Kunming University of Science and Technology.
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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 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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Two-agent single-machine scheduling to minimize the batch delivery cost
TL;DR: This work considers integrated production and batch delivery scheduling in a make-to-order production system involving two competing agents, each of which having its own job set competes to process its jobs on a shared single machine.
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Parallel-machine scheduling of deteriorating jobs with potential machine disruptions ☆
TL;DR: This work considers parallel-machine scheduling of deteriorating jobs in a disruptive environment in which some of the machines will become unavailable due to potential disruptions, and determines the computational complexity status of various cases of the problem, and provides pseudo-polynomial-time solution algorithms and fully polynomial-time approximation schemes.