Dr Jing Jiang is currently a Lecturer at the UTS Priority Research Centre for Artificial Intelligence (CAI), Faculty of Engineering and Information Technology (FEIT) at the University of Technology Sydney (UTS), Australia. She received a PhD degree in Information Technology from UTS in March 2015. Her research interest lies in data mining and machine learning applications with the focuses on deep reinforcement learning and sequential decision-making.
Can supervise: YES
- Data mining and machine learning applications
- Deep reinforcement learning
- Sequential decision making
- Resource optimisation
Xu, Y.L., Jiang, J. & Li, Z. 2011, 'Cyclic Optimisation For Localisation In Freeform Surface Inspection', International Journal Of Production Research, vol. 49, no. 2, pp. 361-374.View/Download from: UTS OPUS or Publisher's site
Increasing demands on precision manufacturing of parts with freeform surfaces have been observed in the last several years. Although significant progress has been made in precision machining of freeform surfaces, inspection of such surfaces remains a dif
Bai, Y., Wang, H., Wu, J., Zhang, Y., Jiang, J. & Long, G. 2016, 'Evolutionary lazy learning for Naive Bayes classification', Proceedings of the International Joint Conference on Neural Networks, IEEE International Joint Conference on Neural Networks, IEEE, Canada, pp. 3124-3129.View/Download from: UTS OPUS or Publisher's site
© 2016 IEEE.Most improvements for Naive Bayes (NB) have a common yet important flaw - these algorithms split the modeling of the classifier into two separate stages - the stage of preprocessing (e.g., feature selection and data expansion) and the stage of building the NB classifier. The first stage does not take the NB's objective function into consideration, so the performance of the classification cannot be guaranteed. Motivated by these facts and aiming to improve NB with accurate classification, we present a new learning algorithm called Evolutionary Local Instance Weighted Naive Bayes or ELWNB, to extend NB for classification. ELWNB combines local NB, instance weighted dataset extension and evolutionary algorithms seamlessly. Experiments on 20 UCI benchmark datasets demonstrate that ELWNB significantly outperforms NB and several other improved NB algorithms.
Hu, R., Pan, S., Long, G., Zhu, X., Jiang, J. & Zhang, C. 2016, 'Co-clustering enterprise social networks', Proceedings of the International Joint Conference on Neural Networks, IEEE International Joint Conference on Neural Networks, IEEE, Vancouver, Canada, pp. 107-114.View/Download from: UTS OPUS or Publisher's site
© 2016 IEEE.An enterprise social network (ESN) involves diversified user groups from producers, suppliers, logistics, to end consumers, and users have different scales, broad interests, and various objectives, such as advertising, branding, customer relationship management etc. In addition, such a highly diversified network is also featured with rich content, including recruiting messages, advertisements, news release, customer complains etc. Due to such complex nature, an immediate need is to properly organize a chaotic enterprise social network as functional groups, where each group corresponds to a set of peers with business interactions and common objectives, and further understand the business role of each group, such as their common interests and key features differing from other groups. In this paper, we argue that due to unique characteristics of enterprise social networks, simple clustering for ESN nodes or using existing topic discovery methods cannot effectively discover functional groups and understand their roles. Alternatively, we propose CENFLD, which carries out co-clustering on enterprise social networks for functional group discovery and understanding. CENFLD is a co-factorization based framework which combines network topology structures and rich content information, including interactions between nodes and correlations between node content, to discover functional user groups. Because the number of functional groups is highly data driven and hard to estimate, CENFLD employs a hold-out test principle to find the group number optimally complying with the underlying data. Experiments and comparisons, with state-of-the-art approaches, on 13 real-world enterprise/organizational networks validate the performance of CENFLD.
Long, G. & Jiang, J. 2013, 'Graph Based Feature Augmentation for Short and Sparse Text Classification', Lecture Notes in Computer Science, International Conference on Advanced Data Mining and Applications, Springer, China, pp. 456-467.View/Download from: UTS OPUS or Publisher's site
Short text classification, such as snippets, search queries, micro-blogs and product reviews, is a challenging task mainly because short texts have insufficient co-occurrence information between words and have a very spare document-term representation. To address this problem, we propose a novel multi-view classification method by combining both the original document-term representation and a new graph based feature representation. Our proposed method uses all documents to construct a neighbour graph by using the shared co-occurrence words. Multi-Dimensional Scaling (MDS) is further applied to extract a low-dimensional feature representation from the graph, which is augmented with the original text features for learning. Experiments on several benchmark datasets show that the proposed multi-view classifier, trained from augmented feature representation, obtains significant performance gain compared to the baseline methods.
Jiang, J., Lu, J., Zhang, G. & Long, G. 2013, 'Optimal Cloud Resource Auto-scaling for Web Application', IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, IEEE/ACM International Symposium on Cluster Computing and the Grid (CCGrid), IEEE, Delft, Netherlands, pp. 58-65.View/Download from: UTS OPUS or Publisher's site
In the on-demand cloud environment, web application providers have the potential to scale virtual resources up or down to achieve cost-effective outcomes. True elasticity and cost-effectiveness in the pay-per-use cloud business model, however, have not yet been achieved. To address this challenge, we propose a novel cloud resource auto-scaling scheme at the virtual machine (VM) level for web application providers. The scheme automatically predicts the number of web requests and discovers an optimal cloud resource demand with cost-latency trade-off. Based on this demand, the scheme makes a resource scaling decision that is up or down or NOP (no operation) in each time-unit re-allocation. We have implemented the scheme on the Amazon cloud platform and evaluated it using three real-world web log datasets. Our experiment results demonstrate that the proposed scheme achieves resource auto-scaling with an optimal cost-latency trade-off, as well as low SLA violations.
Jiang, J., Lu, J. & Zhang, G. 2011, 'An Innovative Self-Adaptive Configuration Optimization System in Cloud Computing', 2011 IEEE Ninth International Conference on Dependable, Autonomic and Secure Computing, International Conference on Dependable, Autonomic and Secure Computing, IEEE, Sydney, Australia, pp. 621-627.View/Download from: UTS OPUS or Publisher's site
Jiang, J., Lu, J., Zhang, G. & Long, G. 2011, 'Scaling-Up Item-Based Collaborative Filtering Recommendation Algorithm Based on Hadoop', 2011 IEEE World Congress on Services (SERVICES 2011), IEEE World Congress on Services, IEEE, Washington, DC, pp. 490-497.View/Download from: UTS OPUS or Publisher's site
Collaborative filtering (CF) techniques have achieved widespread success in E-commerce nowadays. The tremendous growth of the number of customers and products in recent years poses some key challenges for recommender systems in which high quality recommendations are required and more recommendations per second for millions of customers and products need to be performed. Thus, the improvement of scalability and efficiency of collaborative filtering (CF) algorithms become increasingly important and difficult. In this paper, we developed and implemented a scaling-up item-based collaborative filtering algorithm on MapReduce, by splitting the three most costly computations in the proposed algorithm into four Map-Reduce phases, each of which can be independently executed on different nodes in parallel. We also proposed efficient partition strategies not only to enable the parallel computation in each Map-Reduce phase but also to maximize data locality to minimize the communication cost. Experimental results effectively showed the good performance in scalability and efficiency of the item-based CF algorithm on a Hadoop cluster.