Detecting Complex Dependencies in Categorical Data

Tim Oates, Dawn E. Gregory, Paul R. Cohen
Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics, PMLR R0:417-423, 1995.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR0-oates95a, title = {Detecting Complex Dependencies in Categorical Data}, author = {Oates, Tim and Gregory, Dawn E. and Cohen, Paul R.}, booktitle = {Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics}, pages = {417--423}, year = {1995}, editor = {Fisher, Doug and Lenz, Hans-Joachim}, volume = {R0}, series = {Proceedings of Machine Learning Research}, month = {04--07 Jan}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/r0/oates95a/oates95a.pdf}, url = {https://proceedings.mlr.press/r0/oates95a.html}, 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.}, note = {Reissued by PMLR on 01 May 2022.} }
Endnote
%0 Conference Paper %T Detecting Complex Dependencies in Categorical Data %A Tim Oates %A Dawn E. Gregory %A Paul R. Cohen %B Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 1995 %E Doug Fisher %E Hans-Joachim Lenz %F pmlr-vR0-oates95a %I PMLR %P 417--423 %U https://proceedings.mlr.press/r0/oates95a.html %V R0 %X 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. %Z Reissued by PMLR on 01 May 2022.
APA
Oates, T., Gregory, D.E. & Cohen, P.R.. (1995). Detecting Complex Dependencies in Categorical Data. Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research R0:417-423 Available from https://proceedings.mlr.press/r0/oates95a.html. Reissued by PMLR on 01 May 2022.

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