Collaborative information acquisition

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Title: Collaborative information acquisition
Author: Kong, Danxia
Abstract: Increasingly, predictive models are used to support routine business de- cisions and are integral to the strategic competitive business strategies for a wide range of industries. Most often, data-driven predictive models are in- duced from training data obtained through the businesss routine operations. However, recent research on policies for intelligent information acquisitions suggests that proactive acquisition of information can improve models at a lower cost. Most active information acquisition policies are accuracy centric; they aim to identify acquisitions of training data that are particularly benefi- cial for improving the predictive accuracy of a given model. In practice, however, inferences from a predictive model are often used along with inferences from other predictive models as well as constant factors to inform arbitrarily complex decisions. In this dissertation, I discuss how these settings motivate a new kind of collaborative information acquisition (CIA) policies that exploit knowledge of the decision to allow multiple predictive models to collaboratively prioritize the prospective information acquisitions, so as to best improve the decisions they inform jointly. I present a framework for CIA policies and two specific CIA policies: CIA for binary decisions (CIA-BD), and CIA for top-ranked opportu- nities in terms of expected revenue (CIA-TR). Extensive empirical evaluations of the policies on real-world data suggest that the notion of CIA policies is indeed a valuable one. In particular, I demonstrate that these two new poli- cies lead to superior decision-making performances as compared to those of alternative policies that are either decision-centric or do not allow multiple models to collaboratively prioritize acquisitions. The performance exhibited by the CIA policies suggest that these policies are able to effectively exploit knowledge of the decisions to avoid greedy improvements in accuracy of any individual model informing the decisions; instead, they promote improvements in any one or all of the models when such improvements are likely to benefit the decisions.
Department: IROM
Subject: Active information acquisition Active learning Multiple-tasks active learning Decision-centric active learning Active machine learning
URI: http://hdl.handle.net/2152/ETD-UT-2011-12-4678
Date: 2011-12

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