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A binary PSO-based ensemble under-sampling model for rebalancing imbalanced training data Journal article
Jinyan Li, Yaoyang Wu, Simon Fong, Antonio J. Tallón‑Ballesteros, Xin‑she Yang, Sabah Mohammed, Feng Wu. A binary PSO-based ensemble under-sampling model for rebalancing imbalanced training data[J]. Journal of Supercomputing, 2022, 78(5), 7428-7463.
Authors:  Jinyan Li;  Yaoyang Wu;  Simon Fong;  Antonio J. Tallón‑Ballesteros;  Xin‑she Yang; et al.
Favorite | TC[WOS]:18 TC[Scopus]:21  IF:2.5/2.4 | Submit date：2022/05/04
Binary Pso  Ensemble  Imbalanced Classification  Integrity  Multi-objective  Under-sampling  
Dynamic swarm class rebalancing for the process mining of rare events Journal article
Jinyan Li, Yaoyang Wu, Simon Fong, Raymond K. Wong, Victor W. Chu, Kok‑leong Ong, Kelvin K. L. Wong. Dynamic swarm class rebalancing for the process mining of rare events[J]. The Journal of Supercomputing, 2021, 77, 7549-7583.
Authors:  Jinyan Li;  Yaoyang Wu;  Simon Fong;  Raymond K. Wong;  Victor W. Chu; et al.
Favorite | TC[WOS]:0 TC[Scopus]:1  IF:2.5/2.4 | Submit date：2021/03/09
Process Mining  Class Imbalance  Classification  Meta-heuristic  Over-sampling  Under-sampling  
Study of Data Imbalanced Problem in Protein-peptide Binding Prediction Conference paper
Gao, Lu, Siu, Shirley W.I.. Study of Data Imbalanced Problem in Protein-peptide Binding Prediction[C]:ICST, 2020, 61-66.
Authors:  Gao, Lu;  Siu, Shirley W.I.
Favorite | TC[WOS]:1 TC[Scopus]:1 | Submit date：2021/12/06
Protein-peptide Binding Residues  Data Imbalance  Nearmiss  Under-sampling  
Abnormal dynamic functional connectivity and brain states in Alzheimer’s diseases: functional near-infrared spectroscopy study Journal article
Haijing Niu, Zhaojun Zhu, Mengjing Wan, Xuanyu Li, Zhen Yuan, Yu Sun, Ying Han. Abnormal dynamic functional connectivity and brain states in Alzheimer’s diseases: functional near-infrared spectroscopy study[J]. Neurophotonics, 2019, 6(2), 025010.
Authors:  Haijing Niu;  Zhaojun Zhu;  Mengjing Wan;  Xuanyu Li;  Zhen Yuan; et al.
Adobe PDF | Favorite | TC[WOS]:32 TC[Scopus]:32 | Submit date：2022/08/21
CommunicAtion WithIn The BraIn Is Highly Dynamic. AlzheI.e.’s dIseAse (Ad) ExhibIts Dynamic.progression COrrespondIng To a DeclIne In MemOry And Cognition. HoWever, Little Is Known Of wheTher BraIn Dynamic. Are dIsrupted In Ad And Its Prodromal Stage, Mild CognitI.e.impairment (Mci). FOr Our Study, We AcquI.e. High samplIng RAte Functional near-InfrAred Spectroscopy imagIng DAta At Rest From The EntI.e.cOrtex Of 23 pAtients With Ad Dementia, 25 pAtients With Amnestic Mild CognitI.e.impairment (aMci), And 30 age-mAtched Healthy Controls (Hcs). slidIng-wIndow cOrrelAtion And K-means clusterIng Analyses Were Used To Construct Dynamic.Functional Connectivity (Fc) Maps FOr Each Participant. We dIscovered thAt The BraIn’s Dynamic.Fc Variability Strength (q) Significantly IncreAsed In Both aMci And Ad Group As compAred To Hcs. usIng The q Value As a meAsurement, The clAssificAtion perFOrmance ExhibI.e. a Good poWer In differentiAtIng aMci [Area Under The Curve (Auc ¼ 82.5%)] Or Ad (Auc ¼ 86.4%) From Hcs. furThermOre, We Identified Two abnOrmal BraIn Fc stAtes In The Ad Group, Of Which The Occurrence Frequency (f) ExhibI.e. a Significant decreAse FOr The First-level Fc stAte (stAte 1) And a Significant IncreAse FOr The Second-level Fc stAte (stAte 2). We Also Found thAt The abnOrmal f In These Two stAtes Significantly cOrrelAted With The CognitI.e.impairment In pAtients. These fIndIngs provI.e.The First EvI.e.ce To demonstRAte The dIsruptions Of Dynamic.BraIn Connectivity In aMci And Ad And Extend The trAditional stAtic (I.e., tI.e.averaged) Fc fIndIngs In The dIseAse (I.e., dIsconnection Syndrome) And Thus provI.e.Insights InTo UnderstAndIng The pAthophysiological mechanIsms occurrIng In aMci And Ad.  
Similarity majority under-sampling technique for easing imbalanced classification problem Conference paper
Jinyan Li, Simon Fong, Shimin Hu, Raymond K. Wong, Sabah Mohammed. Similarity majority under-sampling technique for easing imbalanced classification problem[C], 2018, 3-23.
Authors:  Jinyan Li;  Simon Fong;  Shimin Hu;  Raymond K. Wong;  Sabah Mohammed
Favorite | TC[WOS]:2 TC[Scopus]:2 | Submit date：2019/02/13
Imbalanced Classification  Under-sampling  Similarity Measure  Smute  
Online Sequential Extreme Learning Machine with Under-Sampling and Over-Sampling for Imbalanced Big Data Classification Conference paper
Du, Jie, Vong, Chi-Man, Chang, Yajie, Jiao, Yang. Online Sequential Extreme Learning Machine with Under-Sampling and Over-Sampling for Imbalanced Big Data Classification[C], GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND:SPRINGER INTERNATIONAL PUBLISHING AG, 2018, 229-239.
Authors:  Du, Jie;  Vong, Chi-Man;  Chang, Yajie;  Jiao, Yang
Favorite | TC[WOS]:4  | Submit date：2018/10/30
Big Data  Imbalance Learning  Os-elm  Under-sampling  Over-sampling  
Rare event prediction using similarity majority under-sampling technique Conference paper
Jinyan Li, Simon Fong, Shimin Hu, Victor W. Chu, Raymond K. Wong, Sabah Mohammed, Nilanjan Dey. Rare event prediction using similarity majority under-sampling technique[C], 2017, 23-39.
Authors:  Jinyan Li;  Simon Fong;  Shimin Hu;  Victor W. Chu;  Raymond K. Wong; et al.
Favorite | TC[Scopus]:5 | Submit date：2019/02/13
Imbalanced Classification  Under-sampling  Similarity Measure  Smute  
Online Sequential Extreme Learning Machine with Under-Sampling and Over-Sampling for Imbalanced Big Data Classification Conference paper
Du, J., Vong, C. M.. Online Sequential Extreme Learning Machine with Under-Sampling and Over-Sampling for Imbalanced Big Data Classification[C], 2017, 229-239.
Authors:  Du, J.;  Vong, C. M.
Favorite |  | Submit date：2022/08/09
Big Data  Imbalance Learning  OS-ELM  Under-sampling  over-sampling  
Adaptive swarm cluster-based dynamic multi-objective synthetic minority oversampling technique algorithm for tackling binary imbalanced datasets in biomedical data classification Journal article
Jinyan Li, Simon Fong, Yunsick Sung, Kyungeun Cho, Raymond Wong, Kelvin K. L. Wong. Adaptive swarm cluster-based dynamic multi-objective synthetic minority oversampling technique algorithm for tackling binary imbalanced datasets in biomedical data classification[J]. BioData Mining, 2016, 9(1).
Authors:  Jinyan Li;  Simon Fong;  Yunsick Sung;  Kyungeun Cho;  Raymond Wong; et al.
Favorite | TC[WOS]:23 TC[Scopus]:31  IF:4.0/3.7 | Submit date：2018/10/30
Imbalanced Dataset  Swarm Optimisation  Under-sampling  Smote  Dynamic Multi-objective  Classification  Biomedical Data