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Prescriptive analytics through constrained bayesian optimization

conference contribution
posted on 2018-01-01, 00:00 authored by Haripriya Harikumar, Santu RanaSantu Rana, Sunil GuptaSunil Gupta, Thin NguyenThin Nguyen, Kaimal Ramachandra, Svetha VenkateshSvetha Venkatesh
Prescriptive analytics leverages predictive data mining algorithms to prescribe appropriate changes to alter a predicted outcome of undesired class to a desired one. As an example, based on the conversation of a reformed addict on a message board, prescriptive analytics may predict the intervention required. We develop a novel prescriptive analytics solution by formulating a constrained Bayesian optimization problem to find the smallest change that we need to make on an actionable set of features so that with sufficient confidence an instance can be changed from an undesirable class to the desirable class. We use two public health dataset, multi-year CDC dataset on disease prevalence across the 50 states of USA and alcohol related data from Reddit to demonstrate the usefulness of our results.

History

Event

Knowledge Discovery and Data Mining. Pacific-Asia Conference (22nd : 2018 : Melbourne, Victoria)

Volume

10937

Series

Lecture Notes in Computer Science

Pagination

335 - 347

Publisher

Springer

Location

Melbourne, Victoria

Place of publication

Cham, Switzerland

Start date

2018-06-03

End date

2018-06-06

ISSN

0302-9743

eISSN

1611-3349

ISBN-13

9783319930398

Language

eng

Grant ID

ARC Australian Laureate Fellowship (FL170100006)

Publication classification

E Conference publication; E1 Full written paper - refereed

Copyright notice

Springer International Publishing AG, part of Springer Nature 2018

Editor/Contributor(s)

Dinh Phung, Vincent Tseng, Geoffrey Webb, Bao Ho, Mohadeseh Ganji, Lida Rashidi

Title of proceedings

PAKDD 2018 : Advances in Knowledge Discovery and Data Mining : Proceedings of 22nd Pacific-Asia Conference