Dr Wenjia Wang
| Job Title | Contact | Location |
|---|---|---|
| Senior Lecturer |
Wenjia dot Wang at uea dot ac dot uk
Tel: +44 (0)1603 59 2577 |
Sciences 2.22 |
Career
Dr. Wang joined the School of Computing Sciences (CMP), UEA, as a senior lecturer in September 2002. He and his PhD students conduct research in the areas of data mining/knowledge discovery, ensemble approach and artificial intelligence. He welcomes and encourages any potentials to contact him to discuss possible research topics.
He has been the Director of MSc in Computer Science since 2003 and the organiser for Masters Dissertation projects, and admissions officer for Overseas applications.
Academic Background
Website
Key Research Interests
Wenjia Wang is part of the Knowledge Discovery and Data Mining Group
Dr Wang has been doing research in the areas of AI, Knowledge Discovery, Data Mining and Operational Research for many years. He holds the memberships of IEEE, IEEE Neural Networks Society and IEEE Computer Science Society.
His currents research interests include
- Artificial neural networks
- Decision tree induction
- Bayesian theory
- Evolutionary computing and Genetic Algorithm
- Machine learning ensemble methodology
- Generic ensemble: topology, diversity, and decision fusion strategies
- Classification ensemble
- Clustering ensemble
- Feature selection ensemble
- Feature Salince Estimation and Feature Selection
- Data/text mining applications
- Heathcare and medical areas
- Bioinformatics
- Finance and insurance
- Security and crime data
- Spam Email classification
- Other areas
Past Research Grants
- EPSRC Grant: "Hybrid Artificial Intelligence Ensemble for Early Detection of Osteoposis
- PI: Dr. Wenjia Wang - Principal Investigator(PI);
- Team: Dr. Sarah Rae - Co-Investigator(CI), Rhumotology Consultant with Bedford Hospital; and Dr. Greame Richards (Research Fellow)
- ESRC Grant: "Identifying Relevant Factors and discovering Complex Patterns in Fuel Poverty"
- PI: Dr. Wenjia Wang(PI),
- Team: Professor Catherine Waddams (School of Management, UEA), Dr. Karl Brazier (Research Fellow)
- EPSRC Grant: "Feature Salience Identification and Methodological Diversity for Improved Prediction"
- PI: Dr. Wenjia Wang(PI),
- Team: Professor Derek Partridge (CI, Exeter University), Dr. Karl Brazier (Research Fellow), and Dr. John Etherington (Defence Service Medical Rehibilitation Centre)
List all publications by Dr Wenjia Wang (Eprints)
Teaching Interests
Dr. Wang teaches a variety of units to both undergraduate and postgratuate courses, including
- Artificial Intelligence,
- Internet Technology,
- Programming (Java) and Applications,
- Research Techniques,
- Research Methods,
- Data Mining.
He is the organiser of MSc Dissertation unit and Research Techniques/Methods units.
Article
Lines, JA, Bagnall, AJ, Caiger-Smith, P and Anderson, S (2011) Classification of Household Devices by Electricity Usage Profiles. Proceedings of the 12th International Conference on Intelligent Data Engineering and Automated Learning. pp. 403-412.
Bian, Shun and Wang, Wenjia (2007) On diversity and accuracy of homogeneous and heterogeneous ensembles. International Journal of Hybrid Intelligent Systems, 4 (2). pp. 103-128. ISSN 1448-5869
Richards, G., Brazier, K. J. and Wang, W. (2006) Feature Salience Definition and Estimation and its Use in Feature Subset Selection. Journal of Intelligent Data Analysis, 10 (1). pp. 3-21. ISSN 1088-467X
Guile, G., Rae, S., Young, A. and Wang, W. (2006) What can we learn from follow-up DEXA scans? Osteoporosis International, 17 (4). p. 422. ISSN 0937-941X
Wang, W., Rae, S. and Richards, G. (2006) Hybrid Data Mining Ensemble for Identifying Osteoporosis Risk Factors and Likelihood. Osteoporosis International, 17. p. 409. ISSN 0937-941X
Wang, W. and Rae, S. (2005) Intelligent Ensemble System Aids Osteoporosis Early Detection. WSEAS Transaction on Systems, 4 (4). pp. 455-560.
Wang, W., Jones, P. and Partridge, D. (2001) A comparative study of feature-salience ranking techniques. Neural Computation, 13 (7). pp. 1603-1623. ISSN 0899-7667
Wang, W. and Brunn, P. (2000) An effective genetic algorithm for job shop scheduling. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 214 (4). pp. 293-300. ISSN 0954-4054
Wang, W., Jones, P. and Partridge, D. (2000) Assessing the impact of input features in a feedforward network. Neural Computing & Applications, 19 (2). pp. 101-112. ISSN 0941-0643
Book Section
Alotaibi, K, Rayward-Smith, V and De La Iglesia, B (2011) Non-metric Multidimensional Scaling for Privacy-Preserving Data Clustering. In: Intelligent Data Engineering and Automated Learning - IDEAL 2011. Spring, pp. 287-298.
Davis, L, Theobald, B-J, Toms, A and Bagnall, A (2011) On the Extraction and Classification of Hand Outlines. In: Proceedings onf the 12th International Conference on Intelligent Data Engineering and Automated Learning. Spring, pp. 92-99.
Harrison, R, Birchall, R, Mann, D and Wang, W (2011) A Novel Ensemble of Distance Measures for Feature Evaluation: Application to Sonar Imagery. In: Intelligent Data Engineering and Automated Learning - IDEAL 2011. Springer, pp. 327-336.
Kirkland, O, Rayward-Smith, V and De La Iglesia, B (2011) A Novel Multi-Objective Genetic Algorithm for Clustering. In: Intelligent Data Engineering and Automated Learning - IDEAL 2011. Springer, pp. 317-326.
Li, Q, Macgregor, AJ and Wang, W (2011) Novel Data Mining Approaches for Detecting Quantitative Trait Loci of Bone Mineral Density in Genome-Wide Linkage Analysis. In: Intelligent Data Engineering and Automated Learning - IDEAL 2011. Springer, pp. 498-510.
Mace, A, Sommariva, R, Fleming, Z and Wang, W (2011) Adaptive K-Means for Clustering Air Mass Trajectories. In: Intelligent Data Engineering and Automated Learning - IDEAL 2011. Spring, pp. 1-8.
Bian, S. and Wang, W. (2007) Ensemble Classification System Implementation for Biomedical Data. In: Life Science Data Mining. Science, Engineering, and Biology Informatics, 2 . World Science Publishers,, pp. 239-256. ISBN 978-981-270-064-3
Wang, Wenjia (2007) Diversity and Accuracy of Data Mining Ensemble. In: Life Science Data Mining. Science, Engineering, and Biology Informatics, 2 . World Science Publishers, pp. 47-72. ISBN 978-981-270-064-3
Wang, W. (2002) Quantifying Relevance of Input Features. In: Intelligent Data Engineering and Automated Learning — IDEAL 2002 Third International Conference Manchester, UK, August 12–14, 2002 Proceedings. Lecture Notes in Computer Science, 2412 . Springer Berlin / Heidelberg, pp. 685-695. ISBN 978-3-540-44025-3
Partridge, D., Wang, W. and Jones, P. (2001) Efficient and Effective Feature Selection in the Presence of Feature Interaction and Noise. In: Advances in Pattern Recognition — ICAPR 2001. Lecture Notes in Computer Science, 2013 . Springer Berlin / Heidelberg, pp. 196-201. ISBN 978-3-540-41767-5
Wang, Wenjia, Jones, Phillis and Partridge, Derek (2000) Diversity between Neural Networks and Decision Trees for Building Multiple Classifier Systems. In: Multiple Classifier Systems. Lecture Notes in Computer Science, 1857 . Springer Berlin / Heidelberg, pp. 240-249. ISBN 978-3-540-67704-8
Conference or Workshop Item
Guile, G. and Wang, W. (2008) Boosting for feature selection for microarray data analysis. In: Proceedings of IEEE WCCI-IJCNN08.
Guile, G. and Wang, W. (2008) Relationships between depth of decision tree and boosting performance. In: Proceedings of IEEE WCCI-IJCNN08.
Wang, W. (2008) Some fundimental issues in ensemble methods. In: Proceedings of IEEE World Congress on Computational Intelligence.
Wang, W. and Cao-Thai, P. (2008) Novel Position coded methods for mining and ranking web access patterns. In: Conference on Intelligence and Security Informatics.
Guile, G. and Wang, W. (2007) Enhancing Boosting by Feature Non-Replacement for Microarray Data Analysis. In: International Joint Conference on Neural Networks, 12-17 Aug. 2007, Orlando, Florida.
Richards, G. and Wang, W. (2006) Investigations on the Characteristics of Random Decision Tree Ensembles. In: IEEE Proceedings of the International Joint Conference on Neural Networks (IJCNN '06), 16-21 July 2006, Vancouver, BC.
Bian, S. and Wang, W. (2006) Investigation on Diversity in Homogeneous and Heterogeneous Ensembles. In: International Joint Conference on Neural Networks, 16-21 July 2006.
Richards, G., Brazier, K. and Wang, W. (2005) The Definition and Estimation of Feature Salience in Databases. In: Conference on Databases and Applications, February 14 – 16, 2005, Innsbruck, Austria.
Rae, S. and Wang, W. (2002) Predicting osteoporosis from risk factors with data mining ensembles. In: International Osteoporosis Foundation: World Congress on Osteoporosis, 10-14 May 2002, Lisbon, Portugal.
Wang, W. (2001) Identifying Salient Risk Factors by Clamping Neural Networks. In: IASTED International Conference on Artificial Intelligence and Applications, Marbella, Spain.
Wang, W., Partridge, D. and Etherington, J. (2001) Hybrid Ensembles and Coincident-Failure Diversity. In: International Joint Conference on Neural Networks (IJCNN '01), 15-19 July 2001, Washington, DC, USA.
Wang, W., Jones, P. and Partridge, D. (2000) Identification of Feature-Salience. In: The IEEE-INNS-ENNS, International Joint Conference on Neural Networks (IJCNN 2000), 24-27 Jul 2000, Como , Italy.
Wang, W. and Partridge, D. (2000) Multiversion systems of neural networks and decision trees. In: IASTED International Conference on Neural Networks (NN'2000), May 15-17, 2000, Pittsburgh, PA, USA.
Partridge, D., Wang, W. and Jones, P. (2000) Artificial intelligence techniques for software system enhancement. In: International ICSC Congress on Intelligent Systems and Applications, Wollongong, Australia.
Key Responsibilities
Deputy Director of Postgraduate Research
Director of MSc Computer Science
Admissions Officer for Overseas Applications
