Simultaneous partitioning and modeling : a framework for learning from complex data

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Title: Simultaneous partitioning and modeling : a framework for learning from complex data
Author: Deodhar, Meghana
Abstract: While a single learned model is adequate for simple prediction problems , it may not be sufficient to represent heterogeneous populations that difficult classification or regression problems often involve . In such scenarios , practitioners often adopt a "divide and conquer" strategy that segments the data into relatively homogeneous groups and then builds a model for each group . This two -step procedure usually results in simpler , more interpretable and actionable models without any loss in accuracy . We consider prediction problems on bi -modal or dyadic data with covariates , e .g . , predicting customer behavior across products , where the independent variables can be naturally partitioned along the modes . A pivoting operation can now result in the target variable showing up as entries in a "customer by product" data matrix . We present a model -based co -clustering framework that interleaves partitioning (clustering ) along each mode and construction of prediction models to iteratively improve both cluster assignment and fit of the models . This Simultaneous CO -clustering And Learning (SCOAL ) framework generalizes co -clustering and collaborative filtering to model -based co -clustering , and is shown to be better than independently clustering the data first and then building models . Our framework applies to a wide range of bi -modal and multi -modal data , and can be easily specialized to address classification and regression problems in domains like recommender systems , fraud detection and marketing . Further , we note that in several datasets not all the data is useful for the learning problem and ignoring outliers and non -informative values may lead to better models . We explore extensions of SCOAL to automatically identify and discard irrelevant data points and features while modeling , in order to improve prediction accuracy . Next , we leverage the multiple models provided by the SCOAL technique to address two prediction problems on dyadic data , (i ) ranking predictions based on their reliability , and (ii ) active learning . We also extend SCOAL to predictive modeling of multi -modal data , where one of the modes is implicitly ordered , e .g . , time series data . Finally , we illustrate our implementation of a parallel version of SCOAL based on the Google Map -Reduce framework and developed on the open source Hadoop platform . We demonstrate the effectiveness of specific instances of the SCOAL framework on prediction problems through experimentation on real and synthetic data .
URI: http : / /hdl .handle .net /2152 /ETD -UT -2010 -05 -1284
Date: 2010-10-11

Citation

Simultaneous partitioning and modeling : a framework for learning from complex data. Doctoral dissertation, University of Texas at Austin. Available electronically from http : / /hdl .handle .net /2152 /ETD -UT -2010 -05 -1284 .

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