Abstract
Locating and evaluating relationships among values in multiple streams of data is a difficult and important task. Consider the data flowing from monitors in an intensive care unit. Readings from various subsets of the monitors are indicative and predictive of certain aspects of the patient’s state. We present an algorithm that facilitates discovery and assessment of the strength of such predictive relationships called Multi-stream Dependency Detection (MSDD). We use heuristic search to guide our exploration of the space of potentially interesting dependencies to uncover those that are significant. We begin by reviewing the dependency detection technique described in [3], and extend it to the multiple stream case, describing in detail our heuristic search over the space of possible dependencies. Quantitative evidence for the utility of our approach is provided through a series of experiments with artificially-generated data. In addition, we present results from the application of our algorithm to two real problem domains: feature-based classification and prediction of pathologies in a simulated shipping network.
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© 1996 Springer-Verlag New York, Inc.
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Oates, T., Schmill, M.D., Gregory, D.E., Cohen, P.R. (1996). Detecting Complex Dependencies in Categorical Data. In: Fisher, D., Lenz, HJ. (eds) Learning from Data. Lecture Notes in Statistics, vol 112. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-2404-4_18
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DOI: https://doi.org/10.1007/978-1-4612-2404-4_18
Publisher Name: Springer, New York, NY
Print ISBN: 978-0-387-94736-5
Online ISBN: 978-1-4612-2404-4
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