M
Man Leung Wong
Researcher at Lingnan University
Publications - 102
Citations - 2448
Man Leung Wong is an academic researcher from Lingnan University. The author has contributed to research in topics: Genetic programming & Bayesian network. The author has an hindex of 27, co-authored 99 publications receiving 2287 citations. Previous affiliations of Man Leung Wong include The Chinese University of Hong Kong & Hong Kong Baptist University.
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Data Mining Using Grammar-Based Genetic Programming and Applications
Man Leung Wong,Kwong-Sak Leung +1 more
TL;DR: Describes a framework, called GGP (Generic Genetic Programming), that integrates GP and ILP based on a formalism of logic grammers that accelerates the learning speed and/or improves the quality of the knowledge induced.
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Machine Learning for Direct Marketing Response Models: Bayesian Networks with Evolutionary Programming
TL;DR: The results suggest that Bayesian networks have distinct advantages over the other methods in accuracy of prediction, transparency of procedures, interpretability of results, and explanatory insight.
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Using evolutionary programming and minimum description length principle for data mining of Bayesian networks
TL;DR: A new approach to learning Bayesian network structures based on the minimum description length (MDL) principle and evolutionary programming is developed, which employs a MDL metric and integrates a knowledge-guided genetic operator for the optimization in the search process.
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An efficient data mining method for learning Bayesian networks using an evolutionary algorithm-based hybrid approach
Man Leung Wong,Kwong-Sak Leung +1 more
TL;DR: A novel data mining approach that employs an evolutionary algorithm to discover knowledge represented in Bayesian networks is proposed, which outperforms MDLEP, the previous algorithm which uses evolutionary programming (EP) for network learning, and other network learning algorithms.
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Evolutionary Computing on Consumer Graphics Hardware
TL;DR: This work proposes implementing a parallel EA on consumer graphics cards, which can find in many PCs, and lets more people use the authors' parallel algorithm to solve large-scale, real-world problems such as data mining.