R
Ran Wang
Researcher at Shenzhen University
Publications - 82
Citations - 2243
Ran Wang is an academic researcher from Shenzhen University. The author has contributed to research in topics: Support vector machine & Computer science. The author has an hindex of 26, co-authored 72 publications receiving 1681 citations. Previous affiliations of Ran Wang include Chinese Academy of Sciences & City University of Hong Kong.
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Stable Matching-Based Selection in Evolutionary Multiobjective Optimization
TL;DR: This paper advocate the use of a simple and effective stable matching (STM) model to coordinate the selection process in MOEA/D and demonstrated that user-preference information can be readily used in the proposed algorithm to find a region that decision makers are interested in.
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Empirical analysis: stock market prediction via extreme learning machine
TL;DR: The design and architecture of the trading signal mining platform that employs extreme learning machine (ELM) to make stock price prediction based on those two data sources concurrently are presented and results show that strategy with more accurate signals will make more profits with less risk.
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A noise-detection based AdaBoost algorithm for mislabeled data
Jingjing Cao,Sam Kwong,Ran Wang +2 more
TL;DR: A new boosting approach, named noise-detection based AdaBoost (ND-AdaBoost), is exploited to combine classifiers by emphasizing on training misclassified noisy instances and correctly classified non-noisy instances.
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Discovering the Relationship Between Generalization and Uncertainty by Incorporating Complexity of Classification
Xizhao Wang,Ran Wang,Chen Xu +2 more
TL;DR: It is concluded that the generalization ability of a classifier is statistically becoming better with the increase of uncertainty when the complexity of the classification problem is relatively high, and thegeneralization ability is statistically become worse with the increases of uncertaintyWhen the complexity is relatively low.
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Incorporating Diversity and Informativeness in Multiple-Instance Active Learning
TL;DR: Two diversity criteria, i.e., clustering-based diversity and fuzzy rough set based diversity, are proposed for MIAL by utilizing a support vector machine (SVM) based MIL classifier and the lower approximations in fuzzy rough sets are used to define a new concept named dissimilarity degree.