—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  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  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 . Besides,
analytical hierarchy process (AHP) of  has been used as
weighting criteria and step wise utility functions of  as
normalization technique. Finally we have compared our results
with that of . As opposed to study , 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
—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.