Gal-Or, Mordechai

May, Jerrold

Spangler, William

English

AIS Electronic Library (AISeL)

Association for Information SystemsAIS Electronic Library (AISeL)AMCIS 2003 Proceedings Americas Conference on Information Systems(AMCIS)December 2003A Multistrategy Data Mining Approach toClassificationMordechai Gal-OrDuquesne UniversityWilliam SpanglerDuquesne UniversityJerrold MayUniversity of PittsburghFollow this and additional works at: http://aisel.aisnet.org/amcis2003This material is brought to you by the Americas Conference on Information Systems (AMCIS) at AIS Electronic Library (AISeL). It has been acceptedfor inclusion in AMCIS 2003 Proceedings by an authorized administrator of AIS Electronic Library (AISeL). For more information, please contactelibrary@aisnet.org.Recommended CitationGal-Or, Mordechai; Spangler, William; and May, Jerrold, "A Multistrategy Data Mining Approach to Classification" (2003). AMCIS2003 Proceedings. 327.http://aisel.aisnet.org/amcis2003/3272516 2003 — Ninth Americas Conference on Information SystemsA MULTISTRATEGY DATA MINING APPROACHTO CLASSIFICATIONMordechai Gal-OrDuquesne Universitygalor@duq.eduWilliam E. SpanglerDuquesne Universityspangler@duq.eduJerrold H. MayUniversity of Pittsburghjerrymay@katz.pitt.eduAbstractOur research explores the use of ensemble, or multistrategy learning techniques for inducing and managing patterns of knowledgefrom organizational data.  Specifically, we are exploring the use of data mining techniques in building an ensemble classificationsystem – i.e., a system that incorporates multiple machine learning techniques to generate multiple models from existing data andmake predictions about new observations.  Our research is inspired and motivated by a real-world business problem.  Theemergence of the digital personal video recorder (PVR) is expected, over time, to cause profound changes in television viewing,as viewers use the new technology to time-shift viewing and skim over or eliminate ‘in stream’ commercials.  This trend is asignificant threat to television advertisers and service providers, because it jeopardizes the traditional means by which advertisingfinances so-called ‘free’ programming.Although a number of modeling methods are potentially useful for the analysis of television viewing data and the classificationof specific viewer types, because of the complexity of the domain we cannot know a priori which methods will be most accuratein specific situations.  The effectiveness of a particular method is dependent on a number of factors, including the characteristicsof the viewer, the prevalence of target viewers in the overall population, the specific viewer attributes to be predicted, asymmetryof misclassification costs, and other characteristics of the viewing data – including types of programs viewed, time of day, andso on.  Because it is unlikely that any single method could perform optimally under these circumstances, we are developing an ensembleclassifier composed of a number of different analytic methods.  This classifier would process various television viewing data setsagainst each of the methods, and attempt to construct a single prediction about the viewer from the collective predictions of thevarious methods.  We have conducted preliminary analyses of viewer data obtained from Nielsen Media Services, Inc. (NMSI),and developed an initial prototype of the data mining component from those analyses.  Our initial study of viewing behavior forfive target gender/age segments suggests that gains in performance are possible even with simple democratic voting schemes –i.e., where each method has a single vote.  Our goal now is to determine whether we can do better by using more sophisticatedcombination strategies.  We intend to approach the problem in two phases.  The first phase will explore the combination ofmultiple methods in a controlled experiment using simulated data, while the second will apply lessons learned from the controlledexperiment to the analysis of actual television viewing data obtained from NMSI.

A Multistrategy Data Mining Approach to Classification

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A Multistrategy Data Mining Approach to Classification

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