Experimental comparison of parametric, non-parametric, and hybrid multigroup classification

Authors:
Dinesh R. Pai;Kenneth D. Lawrence;Ronald K. Klimberg;Sheila M. Lawrence
Affiliations:
Penn State University at Harrisburg, 777 West Harrisburg Pike, Middletown, PA 17057, USA;School of Management, New Jersey Institute of Technology, Newark, NJ 07102, USA;Haub School of Business, Saint Joseph's University, Philadelphia, PA 19066, USA;MSIS Department, Rutgers University, Piscataway, NJ 08854, USA
Venue:
Expert Systems with Applications: An International Journal
Year:
2012

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Abstract

This study evaluates the relative performance of some well-known classification techniques, as well as a proposed hybrid method. The proposed hybrid method is a combination of k-nearest neighbor (kNN) and linear programming (LP) method for four group classification. Computational experiments are conducted to evaluate the performances of these classification techniques. Monte Carlo simulation is used to generate dataset with varying characteristics such as multicollinearity, nonlinearity, etc. for the experiments. The experimental results indicate that LP approaches, in general, and the proposed hybrid method, in particular, consistently have lower misclassification rates for most data characteristics. Furthermore, the hybrid method utilizes the strengths of both methods - k-NN and linear programming - resulting in considerable improvement in the classification accuracy. The results of this study can aid in the design of various hybrid techniques that combine the strengths of different methods to improve classification accuracy and reliability.