All Outputs (7)

Multi-Objective Evolutionary Optimisation for Prototype-Based Fuzzy Classifiers (2022)
Journal Article
Gu, X., Li, M., Shen, L., Tang, G., Ni, Q., Peng, T., & Shen, Q. (2023). Multi-Objective Evolutionary Optimisation for Prototype-Based Fuzzy Classifiers. IEEE Transactions on Fuzzy Systems, 31(5), 1703-1715. https://doi.org/10.1109/tfuzz.2022.3214241

Evolving intelligent systems (EISs), particularly, the zero-order ones have demonstrated strong performance on many real-world problems concerning data stream classification, while offering high model transparency and interpretability thanks to their... Read More about Multi-Objective Evolutionary Optimisation for Prototype-Based Fuzzy Classifiers.

Semantic-Aware Real-Time Correlation Tracking Framework for UAV Videos (2020)
Journal Article
Xue, X., Li, Y., Yin, X., Shang, C., Peng, T., & Shen, Q. (2022). Semantic-Aware Real-Time Correlation Tracking Framework for UAV Videos. IEEE Transactions on Cybernetics, 52(4), 2418-2429. https://doi.org/10.1109/tcyb.2020.3005453

Discriminative correlation filter (DCF) has contributed tremendously to address the problem of object tracking benefitting from its high computational efficiency. However, it has suffered from performance degradation in unmanned aerial vehicle (UAV)... Read More about Semantic-Aware Real-Time Correlation Tracking Framework for UAV Videos.

Visualization of Online Datasets (2017)
Journal Article
Peng, T., & Downie, C. (2017). Visualization of Online Datasets. International Journal of Networked and Distributed Computing, 6(1), 11-23. https://doi.org/10.2991/ijndc.2018.6.1.2

As computing technology advances, computers are being used to orchestrate and advance wide spectrums of commercial and personal life, information visualization becomes even more significant as we immerse ourselves into the era of big data, leading to... Read More about Visualization of Online Datasets.

Feature selection Inspired classifier ensemble reduction. (2014)
Journal Article
Diao, R., Chao, F., Peng, T., Snooke, N., & Shen, Q. (2014). Feature selection Inspired classifier ensemble reduction. IEEE Transactions on Cybernetics, 44, 1259-1268. https://doi.org/10.1109/TCYB.2013.2281820

Classifier ensembles constitute one of the main research directions in machine learning and data mining. The use of multiple classifiers generally allows better predictive performance than that achievable with a single model. Several approaches exist... Read More about Feature selection Inspired classifier ensemble reduction..

A comparison of techniques for name matching (2012)
Journal Article
Peng, T., Li, L., & Kennedy, J. (2012). A comparison of techniques for name matching. GSTF journal on computing, 2,

Information explosion is a problem for everyone nowadays. It is a great challenge to all kinds of businesses to maintain high quality of data in their information applications, such as data integration, text and web mining, information retrieval, sea... Read More about A comparison of techniques for name matching.

A rule based taxonomy of dirty data. (2011)
Journal Article
Li, L., Peng, T., & Kennedy, J. (2011). A rule based taxonomy of dirty data. GSTF journal on computing, 1(2), 140-148

There is a growing awareness that high quality of data is a key to today’s business success and that dirty data existing within data sources is one of the causes of poor data quality. To ensure high quality data, enterprises need to have a process, m... Read More about A rule based taxonomy of dirty data..

A framework for data cleaning in data warehouses (2008)
Journal Article
Peng, T. (2008). A framework for data cleaning in data warehouses. Enterprise Information Systems, 473-478

It is a persistent challenge to achieve a high quality of data in data warehouses. Data cleaning is a crucial task for such a challenge. To deal with this challenge, a set of methods and tools has been developed. However, there are still at least two... Read More about A framework for data cleaning in data warehouses.