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Postboosting Using Extended G-Mean for Online Sequential Multiclass Imbalance Learning Journal article
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2018,Volume: 29,Issue: 12,Page: 6163-6177
Authors:  Vong, Chi-Man;  Du, Jie;  Wong, Chi-Man;  Cao, Jiu-Wen
Favorite  |  View/Download:7/0  |  Submit date:2019/01/17
Dynamic changing distribution  extreme learning machine  imbalance class distribution  multiclass imbalance learning  online sequential learning  
Tackling class overlap and imbalance problems in software defect prediction Journal article
SOFTWARE QUALITY JOURNAL, 2018,Volume: 26,Issue: 1,Page: 97-125
Authors:  Chen, Lin;  Fang, Bin;  Shang, Zhaowei;  Tang, Yuanyan
Favorite  |  View/Download:9/0  |  Submit date:2018/10/30
Software Defect Prediction  Class Imbalance  Class Overlap  Machine Learning  
Post-boosting of classification boundary for imbalanced data using geometric mean Journal article
NEURAL NETWORKS, 2017,Volume: 96,Page: 101-114
Authors:  Du, Jie;  Vong, Chi-Man;  Pun, Chi-Man;  Wong, Pak-Kin;  Ip, Weng-Fai
Favorite  |  View/Download:29/0  |  Submit date:2018/10/30
Imbalance Learning  Boosting  Weighted Elm  Smote  
GOBoost: G-mean optimized boosting framework for class imbalance learning Conference paper
Proceedings of the World Congress on Intelligent Control and Automation (WCICA), Guilin, China, 12-15 June 2016
Authors:  Yang Lu;  Yiu-ming Cheung;  Yuan Yan Tang
Favorite  |  View/Download:11/0  |  Submit date:2019/02/11
Early Diagnosis of Alzheimer Disease Using Instance-Based Learning Techniques Journal article
Journal of Medical Imaging and Health Informatics, 2016,Volume: 6,Issue: 4,Page: 1111-1118
Authors:  Khan, Aunsia;  Liu, Lian-Sheng;  Usman, Muhammad;  Fong, Simon
Favorite  |  View/Download:4/0  |  Submit date:2019/02/13
Alzheimer's Disease  Computer Aided Diagnosis  Machine Learning  
Hybrid Sampling with Bagging for Class Imbalance Learning Conference paper
Lecture Notes in Computer Science (ADVANCES IN KNOWLEDGE DISCOVERY AND DATA MINING), Univ Auckland, Auckland, NEW ZEALAND, APR 19-22, 2016
Authors:  Yang Lu;  Yiu-ming Cheung;  Yuan Yan Tang
Favorite  |  View/Download:2/0  |  Submit date:2019/02/11
Class Imbalance Learning  Ensemble Method  Hybrid Sampling  Sampling Method  
Imbalanced Learning for Air Pollution by Meta-Cognitive Online Sequential Extreme Learning Machine Journal article
Cognitive Computation, 2015,Volume: 7,Issue: 3,Page: 381-391
Authors:  Vong, Chi-Man;  Ip, Weng-Fai;  Chiu, Chi-Chong;  Wong, Pak-Kin
Favorite  |  View/Download:6/0  |  Submit date:2018/11/06
Air Pollution  Meta-cognitive Strategy  Online Sequential Extreme Learning Machine (Os-elm)  Imbalance Data  
Predicting minority class for suspended particulate matters level by extreme learning machine Journal article
Neurocomputing, 2014,Volume: 128,Page: 136
Authors:  Vong C.-M.;  Ip W.-F.;  Wong P.-K.;  Chiu C.-C.
Favorite  |  View/Download:7/0  |  Submit date:2018/10/30
Extreme Learning Machine (Elm)  Imbalance Problem  Pm10  Prior Duplication  Support Vector Machine (Svm)  
In silico prediction of toxic action mechanisms of phenols for imbalanced data with Random Forest learner Journal article
Journal of Molecular Graphics and Modelling, 2012,Volume: 35,Page: 21-27
Authors:  Jing Chen;  Yuan Yan Tang;  Bin Fang;  Chang Guo
Favorite  |  View/Download:4/0  |  Submit date:2019/02/11
Cost-sensitive  Phenols  Qsar  Random Forest  Toxic Action Mechanisms