A comparative assessment of classification methods

Authors:
Melody Y. Kiang
Affiliations:
Information Systems Department, College of Business Administration, California State University, 1250 Bellflower Blvd., Long Beach, CA
Venue:
Decision Support Systems
Year:
2003

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Abstract

Classification systems play an important role in business decision-making tasks by classifying the available information based on some criteria. The objective of this research is to assess the relative performance of some well-known classification methods. We consider classification techniques that are based on statistical and AI techniques. We use synthetic data to perform a controlled experiment in which the data characteristics are systematically altered to introduce imperfections such as nonlinearity, multicollinearity, unequal covariance, etc. Our experiments suggest that data characteristics considerably impact the classification performance of the methods. The results of the study can aid in the design of classification systems in which several classification methods can be employed to increase the reliability and consistency of the classification.