Mr Goce Ristanoski

  • Room: Level: 08 Room: 12
  • Building: Doug McDonell Building
  • Campus: Parkville

Research interests

  • Artificial Intelligence
  • Data Science
  • Machine Learning
  • Medical Data Mining


Goce Ristanoski is a Research Fellow in the School of Computing and Information Systems. He completed his PhD at The University of Melbourne in 2014 in the area of Machine Learning. His work includes applied Machine Learning and Data Science models in academic and industry environments.

Recent publications

  1. Ma, J.; Chan, J.; Ristanoski, G.; Rajasegarar, S.; Leckie, C. Bus travel time prediction with real-time traffic information. Transportation Research Part C: Emerging Technologies. Pergamon Press. 2019, Vol. 105, pp. 536-549. DOI: 10.1016/j.trc.2019.06.008
  2. Ristanoski, G.; Soni, R.; Rajasegarar, S.; Bailey, J.; Leckie, C. Clustering aided support vector machines. International Conference on Machine Learning and Data Mining in Pattern Recognition. Springer International Publishing. 2017, Vol. 10358 LNAI, pp. 322-334. DOI: 10.1007/978-3-319-62416-7_23
  3. Ristanoski, G.; Liu, W.; Bailey, J. A time-dependent enhanced support vector machine for time series regression. Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM Press. 2013, Vol. Part F128815, pp. 946-954. DOI: 10.1145/2487575.2487655
  4. Ristanoski, G.; Liu, W.; Bailey, J. Discrimination Aware Classification for Imbalanced Datasets. Proceedings of the 22nd ACM International Conference on Information & Knowledge Management. ACM Press. 2013, pp. 1529-1532. DOI: 10.1145/2505515.2507836
  5. Ristanoski, G.; Liu, W.; Bailey, J. Time series forecasting using distribution enhanced linear regression. Advances in Knowledge Discovery and Data Mining. Springer Berlin Heidelberg. 2013, Vol. 7818 LNAI, Issue PART 1, pp. 484-495. DOI: 10.1007/978-3-642-37453-1_40
  6. Ristanoski, G.; Bailey, J. Distribution Based Data Filtering for Financial Time Series Forecasting. Lecture Notes in Artificial Intelligence: AI 2011 Advances in Artificial Intelligence, Proceedings. SPRINGER-VERLAG BERLIN. 2011, Vol. 7106, pp. 122-131. DOI: 10.1007/978-3-642-25832-9_13