ABSTRACT


"This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5)"


The ultimate goal of computational intelligence has long been emulating brain-like-intelligence by discovering and learning patterns from data. However, in related research, the data have been assumed to be generated by an underlying fixed physical process. Recently, new algorithms have emerged that can accommodate new data, or data with unbalanced distributions. However, learning from a non-stationary environment, where the underlying process that generates the data changes over time, has received less attention, whereas the problem of learning in a non-stationary environment that incrementally provides unbalanced data has received hardly any attention. Since the brain can and routinely does learn in such settings, the need for a general framework for learning from ? and adapting to ? a nonstationary environment that introduces unbalanced data can be hardly overstated. Spam detection, epidemiological studies, or analysis of climate change, are just a few examples of such scenarios.

Given such a scenario of unbalanced data, the goal of this project is to develop a general framework that would recognize if and when there has been a change, learn novel content, reinforce existing knowledge that is still relevant, and forget what may no longer be relevant. Our hypothesis is that learning from unbalanced and nonstationary data can be achieved by strategic use of i.) data regeneration through local extrapolation ? to help balance the unbalanced dataset ? combined with ii.) an incrementally generated ensemble of experts model that use dynamically assigned weights to emulate short and long term memory properties of the brain ? to help track the changing environments.

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