Informative sampling for large unbalanced data sets
Proceedings of the 10th annual conference companion on Genetic and evolutionary computation, , 2047-2054, 2008
Abstract: Selective sampling is a form of active learning which can reduce the cost of training by only drawing informative data points into the training set. This selected training set is expected to contain more information for modeling compared to random sampling, thus making modeling faster and more accurate. We introduce a novel approach to selective sampling, which is derived from the Estimation-Exploration Algorithm (EEA). The EEA is a coevolutionary algorithm that uses model disagreement to determine the significance of a training datum, and evolves a set of models only on the selected data. The algorithm in this paper trains a population of Artificial Neural Networks (ANN) on the training set, and uses their disagreement to seek new data for the training set. A medical data set called the National Trauma Data Bank (NTDB) is used to test the algorithm. Experiments show that the algorithm outperforms the equivalent algorithm using randomly-selected data and sampling evenly from each class. Finally, the selected training data reveals which features most affect outcome, allowing for both improved modeling and understanding of the processes that gave rise to the data.
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Bongard's work focuses on understanding the general nature of cognition, regardless of whether it is found in humans, animals or robots. This unique approach focuses on the role that morphology and evolution plays in cognition. Addressing these questions has taken him into the fields of biology, psychology, engineering and computer science.
Danforth is an applied mathematician interested in modeling a variety of physical, biological, and social phenomenon. He has applied principles of chaos theory to improve weather forecasts as a member of the Mathematics and Climate Research Network, and developed a real-time remote sensor of global happiness using messages from Twitter: the Hedonometer. Danforth co-runs the Computational Story Lab with Peter Dodds, and helps run UVM's reading group on complexity.
Laurent studies the interaction of structure and dynamics. His research involves network theory, statistical physics and nonlinear dynamics along with their applications in epidemiology, ecology, biology, and sociology. Recent projects include comparing complex networks of different nature, the coevolution of human behavior and infectious diseases, understanding the role of forest shape in determining stability of tropical forests, as well as the impact of echo chambers in political discussions.
Hines' work broadly focuses on finding ways to make electric energy more reliable, more affordable, with less environmental impact. Particular topics of interest include understanding the mechanisms by which small problems in the power grid become large blackouts, identifying and mitigating the stresses caused by large amounts of electric vehicle charging, and quantifying the impact of high penetrations of wind/solar on electricity systems.
Bagrow's interests include: Complex Networks (community detection, social modeling and human dynamics, statistical phenomena, graph similarity and isomorphism), Statistical Physics (non-equilibrium methods, phase transitions, percolation, interacting particle systems, spin glasses), and Optimization(glassy techniques such as simulated/quantum annealing, (non-gradient) minimization of noisy objective functions).