- News & Events
- Resources & Forms
My research areas include computational intelligence, data mining, bioinformatics, web intelligence, and intelligent parallel/distributed computing.
I mainly focus on research in computational intelligence (neural networks, fuzzy logic, evolutionary computation, kernel machines, and swarm intelligence). My research team developed various hybrid intelligent systems based on multiple intelligent techniques for different applications. The primary new intelligent techniques include genetic fuzzy neural networks, granular neural networks, granular support vector machines, granular kernel machines, granular decision trees, granular decision fusion, granular feature selection, computational web intelligence, distributed data mining, and parallel intelligent systems.
Data mining: We designed new methods for imbalanced data classification, feature selection, multiple classification, noise analysis, data fusion, and decision fusion.
Bioinformatics: We implemented useful algorithms for biomarker selection, cancer diagnosis using microarray data, protein structure prediction, protein structure assessment, protein methylation prediction, and biomedical data classification.
Web intelligence: We developed several computational web intelligence systems for e-business, web mining, semantic web, search engines with long query, computing with granular words, NBA scouting, and web-based stock value prediction.
Intelligent parallel/distributed computing: We completed several intelligent parallel/distributed systems, including granular parallel granular neural networks, kernel trees with parallel genetic algorithms, distributed computing applications such as pervasive distributed dynamic sensor data mining system, distributed fuzzy mobile agents for e-commerce, and distributed medical data on grids.
Y. C. Tang, Y.-Q. Zhang, N. V. Chawla, and S. Krasser, SVMs modeling for highly imbalanced classification, IEEE Transactions on Systems, Man, and Cybernetics – Part B, vol. 39, no. 1, pp. 281–288, February 2009.
Y. C. Tang, Y.-Q. Zhang, Z. Huang, X. H. T. Hu, and Y. Zhao, Recursive fuzzy granulation for gene subsets extraction and cancer classification, IEEE Transactions on Information Technology in Biomedicine, vol. 12, no. 6, pp. 723–730, November 2008.
Y.-Q. Zhang, B. Jin, and Y.C. Tang, Granular neural networks with evolutionary interval learning, IEEE Transactions on Fuzzy Systems, Special Section on Granular Computing, vol. 16, no. 2, pp. 309–319, April 2008.
Y. C. Tang, Y.-Q. Zhang, and Z. Huang, Development of two-stage SVM-RFE gene selection strategy for microarray expression data analysis, IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 4, no. 3, pp. 365–381, July-September 2007.
Y.-Q. Zhang, Constructive granular systems with universal approximation and fast knowledge discovery, IEEE Transactions on Fuzzy Systems, vol. 13, no. 1, pp. 48–57, February 2005.
L. X. Yu and Y.-Q. Zhang, Evolutionary fuzzy neural networks for hybrid financial prediction, Special Issue on Knowledge Extraction and Incorporation in Evolutionary Computation, IEEE Transactions on Systems, Man, And Cybernetics (Part C: Applications and Reviews), vol. 35, no. 2, pp. 244–249, May 2005.
Y. C. Tang, B. Jin, and Y.-Q. Zhang, Granular support vector machines with association rules mining for protein homology prediction, Artificial Intelligence in Medicine, Special Issue on Computational Intelligence Techniques in Bioinformatics, vol. 35, no. 1–2, pp. 121–134, September-October 2005.