@techreport{TD:100587,
	att_abstract={{In telecommunications service industry, a group of customers
may be targeted for a set of marketing interests,
and these interests are usually inter-correlated. For example,
churn, upselling and appetency are often considered
together, and decisions on how to retain customers,
and to promote or to upgrade services are associated. Instead
of predicting them separately as univariate models,
we propose an iterative procedure to model multiple responses
prediction into correlated multivariate predicting
scheme. Our correlation factor derivations show that the
exclusive case has more negative correlation factors, which
is always favorable for responses separations in our multivariate
prediction. We also point out that non-exclusive
responses case can be reformed as another exclusive case
via adding the overlapped positive response areas as new
exclusive responses.
This proposed method combines partial least squares
(PLS) method and logistic regressions, in which the former
is used to extract the mutual information from correlations,
while the latter is utilized to refine every single response
prediction through auxiliary information from PLS predictions.
In other words, not only with the given predictor matrix,
but the predicted probability information from other
correlated responses are also inserted to help every single
response prediction. This hybrid regression modeling is
implemented iteratively to refine the prediction gradually.
More importantly, before every round of iteration, all the
positive predictions from different responses compete each
other and the highest values are kept for the only positive
prediction and the others are changed to negative. Here
we exploit the positive exclusive property (i.e., positive for
one response means the negative for others) between multivariate
responses. Numerical results show that with the aid
of mutual information from other responses and the positive
exclusion adjustment, our proposed scheme can improve the
conventional regression models significantly.}},
	att_authors={sa2858, gm1461, rw218j},
	att_categories={},
	att_copyright={{IEEE}},
	att_copyright_notice={{This version of the work is reprinted here with permission of IEEE for your personal use. Not for redistribution. The definitive version was published in IEEE Computer Society's Conference Publishing Services (CPS) . {{, 2012-12-01}}
}},
	att_donotupload={},
	att_private={false},
	att_projects={},
	att_tags={},
	att_techdoc={true},
	att_techdoc_key={TD:100587},
	att_url={http://web1.research.att.com:81/techdocs_downloads/TD:100587_DS1_2011-06-29T20:55:21.431Z.pdf},
	author={Siu-tong Au and Guang-qin Ma and Rensheng Wang},
	institution={{ IEEE Computer Society's Conference Publishing Services (CPS) }},
	month={December},
	title={{Iterative Multivariate Regression Model for Correlated Responses Prediction}},
	year=2012,
}