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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 VenkateshPrescriptive 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
10937Series
Lecture Notes in Computer SciencePagination
335 - 347Publisher
SpringerLocation
Melbourne, VictoriaPlace of publication
Cham, SwitzerlandPublisher DOI
Start date
2018-06-03End date
2018-06-06ISSN
0302-9743eISSN
1611-3349ISBN-13
9783319930398Language
engGrant ID
ARC Australian Laureate Fellowship (FL170100006)Publication classification
E Conference publication; E1 Full written paper - refereedCopyright notice
Springer International Publishing AG, part of Springer Nature 2018Editor/Contributor(s)
Dinh Phung, Vincent Tseng, Geoffrey Webb, Bao Ho, Mohadeseh Ganji, Lida RashidiTitle of proceedings
PAKDD 2018 : Advances in Knowledge Discovery and Data Mining : Proceedings of 22nd Pacific-Asia ConferenceUsage metrics
Categories
Keywords
Prescriptive analyticsBayesian optimizationLinear and nonlinear classifiersConstrained optimizationScience & TechnologyTechnologyComputer Science, Artificial IntelligenceComputer Science, Information SystemsComputer Science, Theory & MethodsComputer ScienceINVERSE CLASSIFICATION PROBLEMInformation SystemsArtificial Intelligence and Image ProcessingDistributed Computing
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