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Seminar
9/4/2014 02:00 pm
CoRE 301

Scalable Learning of Bayesian Network Classifiers

Professor Geoff Webb, Monash University

Organizer(s): Tina Eliassi-Rad

Abstract

I will present our work on highly-scalable out-of-core techniques for learning well-calibrated Bayesian network classifiers.  Our techniques are based on a novel hybrid generative and discriminative learning paradigm.  These algorithms

- provide straightforward mechanisms for managing the bias-variance trade-off

- have training time that is linear with respect to training set size,

- require as few as one and at most four passes through the training data,

- allow for incremental learning,

- are embarrassingly parallelisable,

- support anytime classification,

- provide direct well-calibrated prediction of class probabilities,

- can learn using arbitrary loss functions,

- support direct handling of missing values, and

- exhibit robustness to noise in the training data.

Despite their computationally efficiency, the new algorithms deliver classification accuracy that is competitive with state-of-the-art in-core discriminative learning techniques.

Bio

Geoff Webb is a Professor of Information Technology Research in the Faculty of Information Technology at Monash University, where he heads the Centre for Data Science.  His primary research areas are machine learning, data mining, user modelling and computational structural biology.   His commercial data mining software, Magnum Opus, incorporates many techniques from his association discovery research. Many of his learning algorithms are included in the widely-used Weka machine learning workbench.  He is editor-in-chief of Data Mining and Knowledge Discovery, co-editor of the Springer Encyclopedia of Machine Learning, a member of the advisory board of Statistical Analysis and Data Mining, a member of the editorial board of Machine Learning and was a foundation member of the editorial board of ACM Transactions on Knowledge Discovery from Data.  He is PC Co-Chair of the 2015 ACM SIGKDD International Conference on Knowledge Discovery from Data, was PC Co-Chair of the 2010 IEEE International Conference on Data Mining and General Co-Chair of the 2012 IEEE International Conference on Data Mining. He has received the 2013 IEEE ICDM Service Award and a 2014 Australian Research Council Discovery Outstanding Researcher Award.