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Weka, MOA and Experiment Databases: Frameworks for Machine Learning

Weka, MOA and Experiment Databases: Frameworks for Machine Learning

This video was recorded at The 5th Asian Conference on Machine Learning (ACML), Canberra 2013. This talk is in three parts. The first deals with an aspect of the Weka project that has received little attention, namely the use of machine learning in agricultural applications. I will outline our experiences in this field and present an application development framework which is a direct result of this activity. In particular, one project has met one of the challenges proposed by Kiri Wagstaff at ICML 2012. Second, I will talk about our work in data stream mining with a focus on classification within the Massive Online Analysis framework MOA. After a quick overview of what is in MOA I will present two recent results that indicate a need for caution and a statement of what constitutes state-of-the-art in data stream classification for practitioners. Finally, I will present the idea of experiment databases, a framework for machine learning experimentation that saves effort and offers opportunities for meta learning and hypothesis generation.


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