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|Title: ||Hierarchical Classifier-Regression Ensemble for Multi-Phase Non-Linear Dynamic System Response Prediction: Application to Climate Analysis|
|Authors: ||Gonzalez, Doel L.|
Tetteh, Isaac K.
Samatova, Nagiza F.
|Issue Date: ||2012|
|Citation: ||2012 IEEE 12th International Conference on Data Mining Workshops|
|Abstract: ||A dynamic physical system often undergoes phase
transitions in response to fluctuations induced on system parameters. For example, hurricane activity is the climate system’s
response initiated by a liquid-vapor phase transition associated
with non-linearly coupled fluctuations in the ocean and the
atmosphere. Because our quantitative knowledge about highly
non-linear dynamic systems is very meager, scientists often resort
to linear regression techniques such as Least Absolute Deviation
(LAD) to learn the non-linear system’s response (e.g., hurricane
activity) from observed or simulated system’s parameters (e.g.,
temperature, precipitable water, pressure). While insightful, such
models still offer limited predictability, and alternatives intended
to capture non-linear behaviors such as Stepwise Regression
are often controversial in nature. In this paper, we hypothesize
that one of the primary reasons for lack of predictability is the
treatment of an inherently multi-phase system as being phaseless.
To bridge this gap, we propose a hybrid approach that first
predicts the phase the system is in, and then estimates the
magnitude of the system’s response using the regression model
optimized for this phase. Our approach is designed for systems
that could be characterized by multi-variate spatio-temporal data
from observations, simulations, or both.|
|Description: ||An article published by 2012 IEEE 12th International Conference on Data Mining Workshops and available at DOI 10.1109/ICDMW.2012.133|
|Appears in Collections:||College of Science|
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