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We introduce a novel incremental decision tree learning algorithm, Hoeffding Anytime Tree, that is statistically more efficient than the current state-of-the-art, Hoeffding Tree. We demonstrate that an implementation of Hoeffding Anytime Tree–-"Extremely Fast Decision Tree", a minor modification to the MOA implementation of Hoeffding Tree–-obtains significantly superior prequential accuracy on most of the largest classification datasets from the UCI repository. Hoeffding Anytime Tree produces the asymptotic batch tree in the limit, is naturally resilient to concept drift, and can be used as a higher accuracy replacement for Hoeffding Tree in most scenarios, at a small additional computational cost.

Download the paper here.

Download the software here.

 

The Living with AI discussion series will feature conversations about social and ethical implications of artificial intelligence. Join us for the first of these conversations, featuring AI expert Terry Cailli addressing the question "Can Computers Behave Ethically?"

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Fantastic planning session at the EODC. Amazing problems for time series analysis, starting with how to scale the state-of-the-art from being challenged by datasets containing 10 thousand series to routinely handling 10 trillion. Improved analytics for earth observation data have massive expected benefits for environmental monitoring, agriculture and economic planning, to name but a few. As always, it is both scary and exciting to be at the start of a massive undertaking.

With EODC Head of Science Wolfgang Wagner and my Monash earth observation colleague Christoph Rüdiger.

With EODC Managing Director Christian Briese and Software Developer Thomas Mistelbauer.

I am excited to be joining the extraordinary team at Froomle as a Technical Advisor and looking forward to helping refine our best of class recommendation engine.

Why not join us at the Fourth International Conference on Information Retrieval and Knowledge Management to be held in Kota Kinabalu, Malaysia from 26 – 28 March 2018: http://camp18.pecamp.org/?

I am deeply honoured to have received this prestigious award  from the Australian Museum. It is deeply appreciated recognition of not just my research, but also of the many talented researchers with whom I have been privileged to collaborate.

Details of the award can be found here.

We are looking for a talented postdoc to work with us on a number of research projects on Time Series Classification and Machine Learning from Data Streams. Details here.

With the second satellite of the Sentinel-2 mission just launched in 2017, there is an incredible opportunity for the right student to become the world leader on how to analyse and make sense of this vast amount of data. It is anticipated that the project will make contributions to the theory of machine learning, with applications to the study of vegetation in general and more specifically in agriculture. The project however remains open if the successful candidate has other applications at heart (eg landslide, fire prediction). This project is fully funded. There is a paradigm shift in the way we can observe our planet: new-generation satellites (Sentinel-2, Landsat-8) are now imaging Earth completely, every 5 days, at high resolution, and at _no charge to end-users. It is not yet possible to tap the full value of this data, as existing machine learning methods for classifying time series cannot scale to such vast volumes of data. Temporal land-cover maps assign unique labels to geographic areas, describing their evolution over time. One of today’s key challenges is how to automatically produce these maps from the growing torrent of satellite data, to monitor Earth’s highly dynamic systems [a-h]. Presently, state-of-the-art research into time series classification lags behind the demands of the latest space missions, which produce terabytes of data each day. Why? Most of the research into time series classification has been done with datasets that hold no more than 10 thousand time series [i]. In contrast, the Sentinel-2 satellite gathers over 10 *trillion* time series, capturing Earth’s land surfaces and coastal waters at resolutions of 10-60m. Although much research has gone into classifying remote sensing images, few studies have analysed time series extracted from sequences of satellite images. This Project aims to create the machine learning technologies necessary to analyse series of satellite images, and to produce accurate temporal land-cover maps of our planet. Potential high-value applications for Australia include fire prevention, agricultural planning, and mining site monitoring and rehabilitation.

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I am honoured to have been shortlisted for the inaugural Eureka Prize for Excellence in Data Science for my ARC funded project "Combining generative and discriminative strategies to facilitate efficient and effective learning from big data."