• ISSN: 2301-3567
    • Frequency: Quarterly (2013-2014); Monthly (Since 2015)
    • DOI: 10.18178/JOEBM
    • Editor-in-Chief: Prof. Eunjin Hwang
    • Executive Editor: Ms Jessica C. Xiao
    • Abstracting/ Indexing: DOAJ, Engineering & Technology Library,  Electronic Journals Library, Ulrich's Periodicals Directory, MESLibrary, Google Scholar, Crossref, and ProQuest.
    • E-mail: joebm@ejournal.net
JOEBM 2014 Vol.2(1): 62-67 ISSN: 2301-3567
DOI: 10.7763/JOEBM.2014.V2.100

Composite Indicators for Data Mining: A New Framework for Assessment of Prediction Classifiers

Shahid Anjum
Abstract—Effectiveness and superiority of predictive accuracy of different Data mining (DM) models over the others have traditionally come from results of the empirical studies of DM. Study [4] compared logistic regression, classification tree, neural network, random forest and AdaBoost based on evaluation composite indicators (ECI) built from four parameters like accuracy, interpretability, robustness and speed using four input alternatives (original, aggregated, principal component analysis and stacking based variables), three random indicator weighting criteria and two indicator normalization methods (z-score and min-max). In this study, ECI has been calculated using results from [4] from same four input variable types but using “four plus one” (five) parameters. The fifth parameter of interest (POI) named as Residual Efficiency (RE), has been quantified for this study based on characteristics of interest (COI) described in [10]. Besides, analytical hierarchy process (AHP) of [13] has been used as weighting criteria and step wise utility functions of [12] as normalization technique. Finally we have compared our results with that of [4]. As opposed to study [4], this study has calculated ECIs for all the classifiers used and results have narrower ranges thus are more realistic for comparing the considered classifiers objectively based on type of inputs and POIs.

Index Terms—Knowledge discovery and data mining (KDD), analytical hierarchy process (AHP), evaluation composite indicators (ECI), multi-criteria decision making (MCDM).

S. Anjum is with Faculty member (Finance and MIT) for Doctor of Management in Information Technology (DMIT) and Doctor of Business Administration (DBA) programs at College of Management, Lawrence Technological University (LTU), Buell Bldg., M331, 21000 West Ten Mile Road, Southfield, MI 48075-1058, USA (e-mail: anjumsw@ hotmail.com).


Cite:Shahid Anjum, "Composite Indicators for Data Mining: A New Framework for Assessment of Prediction Classifiers," Journal of Economics, Business and Management vol. 2, no. 1, pp. 62-67, 2014.

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