Exploiting multiple classifier types with active learning
Proceedings of the 11th Annual conference on Genetic and evolutionary computation, , 1905-1906, 2009
Abstract: Many approaches to active learning involve training one classifier by periodically choosing new data points about which the classifier has the least confidence, but designing a confidence measure without bias is nontrivial. An alternative approach is to train an ensemble of classifiers by periodically choosing data points that cause maximal disagreement among them. Many classifiers with different underlying structures could fit this framework, but some classifiers are more suitable for different data sets than others. The question then arises as to how to find the most suitable classifier for a given data set. In this work, an evolutionary algorithm is proposed to address this problem. The algorithm starts with a combination of artificial neural networks and decision trees, and iteratively adapts the ratio of the classifier types according to a replacement strategy. Experiments with synthetic and real data sets show that when the algorithm considers both fitness and classifier type for replacement, the population becomes saturated with accurate instantiations of the more suitable classifier type. This allows the algorithm to perform consistently well across data sets, without having to determine a priori a suitable classifier type.
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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).