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Yue-Shi Lee
Researcher at Ming Chuan University
Publications - 110
Citations - 1792
Yue-Shi Lee is an academic researcher from Ming Chuan University. The author has contributed to research in topics: Web mining & Association rule learning. The author has an hindex of 18, co-authored 103 publications receiving 1550 citations. Previous affiliations of Yue-Shi Lee include National Taiwan University & National Central University.
Papers
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Journal ArticleDOI
Cluster-based under-sampling approaches for imbalanced data distributions
Show-Jane Yen,Yue-Shi Lee +1 more
TL;DR: Cluster-based under-sampling approaches for selecting the representative data as training data to improve the classification accuracy for minority class are proposed and the experimental results show that these approaches outperform the other under-Sampling techniques in the previous studies.
Book ChapterDOI
Under-Sampling Approaches for Improving Prediction of the Minority Class in an Imbalanced Dataset
Show-Jane Yen,Yue-Shi Lee +1 more
TL;DR: This paper proposes cluster-based under-sampling approaches for selecting the representative data as training data to improve the classification accuracy for minority class in the imbalanced class distribution problem.
Journal ArticleDOI
Robust and efficient multiclass SVM models for phrase pattern recognition
TL;DR: This paper introduces the proposed two new multiclass SVM models that make the system substantially faster in terms of training and testing while keeps the SVM accurate and can be applied to similar tasks such as named entity recognition and Chinese word segmentation.
Book ChapterDOI
Mining high utility quantitative association rules
Show-Jane Yen,Yue-Shi Lee +1 more
TL;DR: A data mining algorithm to find high utility itemsets with purchased quantities, from which high utility quantitative association rules also can be generated, which is more efficient than other algorithms which only discovered high utility association rules.
Journal ArticleDOI
A support vector machine-based context-ranking model for question answering
TL;DR: A machine learning-based question-answering framework, which integrates a question classifier, simple document/passage retrievers, and the proposed context-ranking models, which provides flexible features to learners.