Dr Yun Sing Koh

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Senior Lecturer

Research | Current

My current research interests include

  • Data mining
  • Machine learning
  • Information retrieval.

Most of my current research revolves around finding rare patterns/rules within datasets. The aim is to find expensive rules that are of significance for users. I have been looking at different ways to generate rare association rules. I have also developed a keen interest in several other areas including data stream mining and online auction fraud detection.

Postgraduate supervision

Current PhD Supervision / Co-Supervision

Alex (Yuxuan) Peng (2017) Deep Learning (co-supervised with Dr Pat Riddle)

Diana Benavides Prado (2016) - Meta Learning and Transfer Learning (co-supervised with Dr Pat Riddle)

Robert Anderson (2016) - Data Stream Mining (co-supervised with Prof Gill Dobbie)

Monica Bian (2015) - Social Network Mining (co-supervised with Prof Gill Dobbie)

Ian Wong (2016) - Feature Selection and Engineering (co-supervised with Prof Gill Dobbie)

Areas of expertise

Machine learning specifically in the area of unsupervised learning, data stream mining, and anomaly detection.

Selected publications and creative works (Research Outputs)

  • Samimi, P., Ravana, S. D., Webber, W., & Koh, Y. S. (2017). Effects of objective and subjective competence on the reliability of crowdsourced relevance judgments. Information Research, 22 (1).
  • Samimi, P., Ravana, D., & Koh, Y. S. (2016). Effect of verbal comprehension skill and self-reported features on reliability of crowdsourced relevance judgments. COMPUTERS IN HUMAN BEHAVIOR, 64, 793-804. 10.1016/j.chb.2016.07.058
  • Koh, Y. S. (2016). CD-TDS: Change detection in transactional data streams for frequent pattern mining. Proceedings of the International Joint Conference on Neural Networks. 10.1109/IJCNN.2016.7727383
  • Koh, Y. S., & Ravana, S. D. (2016). Unsupervised rare pattern mining: A survey. ACM Transactions on Knowledge Discovery from Data, 10 (4).10.1145/2898359
    URL: http://hdl.handle.net/2292/29892
  • Anderson, R., Koh, Y. S., & Dobbie, G. (2016). CPF: Concept profiling framework for recurring drifts in data streams. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 10.1007/978-3-319-50127-7_17
    Other University of Auckland co-authors: Gill Dobbie
  • Jeong, S. Y., Koh, Y. S., & Dobbie, G. (2016). Phishing Detection on Twitter Streams. Paper presented at 20th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), Auckland, NEW ZEALAND. 19 April 2016. TRENDS AND APPLICATIONS IN KNOWLEDGE DISCOVERY AND DATA MINING (PAKDD 2016). (pp. 13). 10.1007/978-3-319-42996-0_12
    Other University of Auckland co-authors: Gill Dobbie
  • Chen, K., Koh, Y. S., & Riddle, P. (2016). Proactive Drift Detection: Predicting Concept Drifts in Data Streams using Probabilistic Networks. Paper presented at International Joint Conference on Neural Networks (IJCNN), Vancouver, CANADA. 24 July - 29 July 2016. 2016 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN). (pp. 8).
    Other University of Auckland co-authors: Patricia Riddle
  • Koh, Y. S., & Pears, R. (2015). HI-Tree: Mining High Influence Patterns Using External and Internal Utility Values. Paper presented at 17th International Conference on Big Data Analytics and Knowledge Discovery (DaWaK), Valencia, SPAIN. 1 September - 4 September 2015. BIG DATA ANALYTICS AND KNOWLEDGE DISCOVERY. (pp. 14). 10.1007/978-3-319-22729-0_4


Contact details

Primary location

Level 4, Room 485
New Zealand

Web links