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

Dinesh R. Pai, Kenneth D. Lawrence, Ronald K. Klimberg, Sheila M. Lawrence

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)8593-8603
Number of pages11
JournalExpert Systems with Applications
Issue number10
StatePublished - Aug 2012

All Science Journal Classification (ASJC) codes

  • General Engineering
  • Computer Science Applications
  • Artificial Intelligence


  • Artificial neural net
  • Discriminant analysis
  • Hybrid
  • K-NN
  • Linear programming
  • Logistic regression
  • Multi-group classification
  • Neural networks


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