MINING MINIMAL CONSTRAINED FLOW CYCLES FROM COMPLEX TRANSACTION DATA
Contact CMCRC for this article Author(s): Meng Xu, Michael Bain
RECENT ADVANCES OF EXCEPTION MINING IN STOCK MARKET
Contact CMCRC for this article Author(s): Chao Luo, Yanchang Zhao, Dan Luo, Yuming Ou, Li Liu
MINING EXCEPTIONAL ACTIVITY PATTERNS IN MICROSTRUCTURE DATA
Contact CMCRC for this article Author(s): Yuming Ou, Longbing Cao, Chao Luo and Li Liu
A LEARNING PROCESS USING SVMS FOR MULTI-AGENTS DECISION CLASSIFICATION
Contact CMCRC for this article Author(s): Yanshan Xiao, Feiqi Deng, Bo Liu, Shouqiang Liu, Dan Luo, Guohua Liang
DECISION TREES FOR IMBALANCED DATA SETS
Contact CMCRC for this article Author(s): Wei Liu, Sanjay Chawla
EXCHANGE TRADING RULES, SURVEILLANCE AND INSIDER TRADING
Are rules and their enforcement effective at mitigating insider trading? A study shows that rules and surveillance together have the potential to mitigate the perpetration of market manipulation but also to exacerbate the profits from such manipulative activities. Insider trading can be facilitated by several forms of market manipulation that are not, strictly speaking, by
OUTLIER MINING ON MULTIPLE TIME SERIES DATA IN STOCK MARKET
Contact CMCRC for this article Author(s): Chao Luo, Yanchang Zhao, Dan Luo, Yuming Ou, Li Liu
EXCEPTION MINING ON MULTIPLE TIME SERIES IN STOCK MARKET
Contact CMCRC for this article Author(s): Chao Luo, Longbing Cao, Yuming Ou, Zhengqi Zhang
TIKHONOV OR LASSO REGULARIZATION: WHICH IS BETTER AND WHEN. IN TOOLS WITH ARTIFICIAL INTELLIGENCE
pages 795–802. IEEE, 2013. It is well known that supervised learning problems with ℓ1 (Lasso) and ℓ2 (Tikhonov or Ridge) regularizers will result in very different solutions. For example, the ℓ1 solution vector will be sparser and can potentially be used both for prediction and feature selection. However, given a data set it is often hard to determine
DOMAIN-DRIVEN LOCAL EXCEPTIONAL PATTERN MINING FOR
Contact CMCRC for this article Author(s): Yuming Ou, Longbing Cao, Chao Luo and Chengqi Zhang