Welcome Prof.Gang Wang from HeFei University of Technology to be Commitee Member!

日期:2020-03-13 点击量:  287次


Prof.Gang Wang

School of Management, HeFei University of Technology,China(click)

Research Area:

Information Systems, Business Analytics

Research Experience:

Ø Gang Wang is a Professor in the School of Management, HeFei University of Technology. He received his Ph. D. in the School of Management, FuDan University. His current research focues on Data Mining and Business Intelligence, Ensemble Learning, Socical Network Analysis. His past research has been published in DSS, IEEE Intelligent Systems, IP&M, and KBS.

Ø Associate Editor: Decision Suuport Systems.

Ø Editorial Board Member: London Journals Press,Intelligent Information Management,Journal of Autonomous Intelligence.

(1) Hongcheng gan, June Wei, Gang Wang. A Generic Work Zone Evaluation Tool Driven by Macroscopic Traffic Simulation Model. International Journal of Mobile Communications. (In Press)

(2) Jing Liu, Gang Wang, Gang Chen. Identifying Adverse Drug Events from Social Media using an Improved Semi-Supervised Method. IEEE Intelligent Systems. 2019, 34(2): 66-74.

(3) Ying Yang, Jun Wang, Gang Wang, YuWang Chen. Research and Development Project Risk Assessment Using a Belief Rule-based System with Random Subspaces. Knowledge-Based Systems. 2019, 178(15): 51-60.

(4) Gang Wang, Daqing Zheng, Shanlin Yang, Jian Ma. FCE-SVM: A New Cluster Based Ensemble Method for Opinion Mining from Social Media. Information Systems and e-Business Management, 2018, 16(4):721-742. 

(5) Jing Liu, Gang Wang. Pharmacovigilance from social media: An improved random subspace method for identifying adverse drug events. International Journal of Medical Informatics, 2018, 117(9): 33-43.

(6) Gang Wang, Gang Chen, Yan Chu. A new Random Subspace Method incorporating sentiment and textual information for financial distress prediction. Electronic Commerce Research and Applications. 2018, 29(1): 30-49.

(7) Gang Wang, XiRan He, Carolyne Isigi Ishuga. HAR-SI: A Novel Hybrid Article Recommendation Approach Integrating with Social Information in Scientific Social Network. Knowledge-Based Systems. 2018, 148(1): 85-99.

(8) Jing Liu, Songzheng Zhao, Gang Wang. SSEL-ADE: A semi-supervised ensemble learning framework for extracting adverse drug events from social media. Artificial Intelligence in Medcine. 2018, 84(1):34-49. 

(9) Wang Gang, He XiRan, Ishuga Carolyne Isigi. Social and content aware One-Class recommendation of papers in scientific social networks. PLOS ONE, 2017, 12(8): 1-30.

(10) Wang Gang, Zhang Zhu, Sun Jianshan, Yang Shanlin, et al, POS-RS: A Random Subspace method for sentiment classification based on part-of-speech analysis, Information Processing and Management, 2015, 51(4): 458-479. 

(11) Sun Jianshan, Wang Gang, et al, Mining affective text to improve social media item recommendation, Information Processing and Management, 2015,51(4):444-457. 

(12) Wang Gang, Sun Jianshan, Ma Jian, et al. Sentiment classification: the contribution of ensemble learning. Decision Support Systems, 2014, 57(1): 77-93.

(13) Wang Gang, Ma Jian, Yang Shanlin. An Improved Boosting Based on Feature Selection for Corporate Bankruptcy Prediction. Expert Systems with Applications, 2014, 41(5): 2353-2361.

(14) Wang Gang, Ma Jian. A Hybrid Ensemble Approach for Enterprise Credit Risk Assessment Based on Support Vector Machine. Expert Systems with Applications, 2012, 39(5): 5325-5331.

(15) Wang Gang, Ma Jian, Huang Lihua, Xu Kaiquan. Two Credit Scoring Models Based on Dual Strategy Ensemble Trees. Knowledge Based Systems, 2012, 26(1): 61-68.

(16) Wang Gang, Ma Jian, Yang Shanlin. Study of Corporate Credit Risk Prediction Based on Integrating Boosting and Random Subspace. Expert Systems with Applications. 2011, 38, 13871-13878.

(17) Wang Gang, Ma Jian, Yang Shanlin. IGF-Bagging: information gain based feature selection for bagging. International Journal of Innovative Computing, Information and Control. 2011, 7(11): 6247-6259. 

(18) Wang Gang, Hao Jinxing, Ma Jian, Jiang Hongbin. A comparative assessment of ensemble learning for credit scoring. Expert Systems with Applications, 2011, 38(1): 223-230.