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Prescriptive Analytics Through Constrained Bayesian Optimization

Publication Type : Conference Paper

Thematic Areas : Learning-Technologies

Publisher : Advances in Knowledge Discovery and Data Mining, Springer International Publishing.

Source : Advances in Knowledge Discovery and Data Mining, Springer International Publishing, Cham (2018)

Url :

ISBN : 9783319930343

Keywords : Bayesian optimization, Constrained optimization, Linear and nonlinear classifiers, Prescriptive analytics

Campus : Coimbatore

School : School of Engineering

Center : Amrita Center For Research in Analytics, Technologies & Education (AmritaCREATE)

Department : Sciences

Year : 2018

Abstract : 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.

Cite this Research Publication : H. Harikumar, Rana, S., Gupta, S., Nguyen, T., Kaimal, R., and Venkatesh, S., “Prescriptive Analytics Through Constrained Bayesian Optimization”, in Advances in Knowledge Discovery and Data Mining, Cham, 2018.

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