Goal Programming Formulation for a Comparative Analysis for a Comparative Analysis of Scalar Norms and Ordinal vs. Ratio Data

Goal programming has proven a valuable mathematical programming form in a number of venues.

There has been a similar rapid growth in interest in data mining, where a variety of different data types

are encountered. This paper applies goal programming formulations to compare relative performance

of L|, L2, and L^ norms as well as ordinal and ratio data types in a dynamic predictive environment.

The models are applied to compare relative accuracy and stability in forecasting a professional athletic

environment. Results confirm that ratio data provide more accurate forecasts than ordinal data.

Responsiveness to error can be good and bad in prediction. Too much response to outlying events

makes the predictor "nervous" and unreliable, L, metric models are much easier and faster to solve,

but involve higher levels of ambiguity than nonlinear models, L] metric models also were more

responsive to changes, but correspondingly tend to be more affected by unexpected outcomes.

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	Lee, Sang M Emeritus Expertise: Entrepreneurship Information Systems International - Europe International - South and Central America International - Asia Management CBA 251 P.O. Box 880491 University of Nebraska-Lincoln Lincoln, NE 68588-0491, USA Phone: (402) 472-3915 Fax: (402) 472-5855 slee1@unl.edu	Lee, Sang M	Management
	Olson, David L James and H.K. Stuart Chancellors Distinguished Chair Expertise: Information Systems Operations Management Management CBA 256 P.O. Box 880491 University of Nebraska-Lincoln Lincoln, NE 68588-0491, USA Phone: (402) 472-4521 Fax: (402) 472-5855 dolson3@unl.edu	Olson, David L	Management