FOR PARTICIPANTS

Keynote

Speaker: Prof. Bernhard Pfahringer
Title: Partial solutions for some current challenges in stream mining

Stream mining is concerned with online learning from non-stationary data sources. I will argue that many, if not all, big data mining endeavours are instances of stream mining. This presentation will highlight issues in stream mining, including proper evaluation, temporal dependencies, label acquisition, and preprocessing, and will present some preliminary solutions for these challenges. Bernhard Pfahringer received his PhD degree from the University of Technology in Vienna, Austria, in 1995. He is a Professor with the Department of Computer Science at the University of Waikato in New Zealand. His interests span a range of data mining and machine learning sub-fields, with a focus on streaming, randomization, and complex data.

Programme (NEW)

Papers

• Joel Janek Dabrowski, Ashfaqur Rahman and Andrew George, Prediction of Dissolved Oxygen from pH and Water Temperature in Aquaculture Prawn Ponds
• Weixi Mao, Xiaohang Liu, Jeremiah Deng, Xiaobin Hua and Ralf Ohlemüller, Ground-based Tree Image Data Mining for Phenology Research
• Xin Chi, Jianfeng Yao and Huiming Yu, A Hybrid Load Balance Method Using Evolutionary Computing
• Loren Kersey, Noel Park and Kat Lilly, ECG heartbeat classification: an exploratory study