Predicting Behavior

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

25 Scopus Citations
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Detail(s)

Original languageEnglish
Pages (from-to)35-43
Journal / PublicationIEEE Intelligent Systems
Volume30
Issue number3
Publication statusPublished - 1 May 2015

Abstract

Behavior prediction has become an important area of emphasis, with applications ranging from e-commerce, marketing analytics, and financial forecasting to smart health, security informatics, and crime prevention. However, traditional behavior modeling approaches have shortcomings: heavy reliance on objective, observed data, and a failure to consider the granular, micro-level decisions and actions that collectively drive macro-level behavior. To address these shortcomings, the authors present a behavior prediction framework that advocates the integration of objective and perceptual information and decomposes behavior into a series of closely interrelated stages to facilitate enhanced behavior prediction performance. The utility of the framework is demonstrated through a series of experiments pertaining to prediction of auction fraud, e-commerce conversions, and customer churn.

Research Area(s)

  • artificial intelligence, behavior prediction, Computer crime, data mining, Drugs, Intelligent systems, intelligent systems, Kernel, machine learning, predictive analytics, Predictive models, Support vector machines

Citation Format(s)

Predicting Behavior. / Abbasi, Ahmed; Lau, Raymond Y.K.; Brown, Donald E.
In: IEEE Intelligent Systems, Vol. 30, No. 3, 01.05.2015, p. 35-43.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review