Abstract: An open question in ensemble-based active learning is how to choose one classifier type, or appropriate combinations of multiple classifier types, to construct ensembles for a given task. While existing approaches typically choose one classifier type, this paper presents a method that trains and adapts multiple instances of multiple classifier types toward an appropriate ensemble during active learning. The method is termed adaptive heterogeneous ensembles (henceforth referred to as AHE). Experimental evaluations show that AHE constructs heterogeneous ensembles that outperform homogeneous ensembles composed of any one of the classifier types, as well as bagging, boosting and the random subspace method with random sampling. We also show in this paper that the advantage of AHE over other methods is increased if (1) the overall size of the ensemble also adapts during learning; and (2) the target data set is composed of more than two class labels. Through analysis we show that the AHE outperforms other methods because it automatically discovers complementary classifiers: for each data instance in the data set, instances of the classifier type best suited for that data point vote together, while instances of the other, inappropriate classifier types disagree, thereby producing a correct overall majority vote.
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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).