Computers and Operations Research
A simple weighting scheme for classification in two-group discriminant problems
Computers and Operations Research
A comparative assessment of classification methods
Decision Support Systems
Neural Computing and Applications
Review: Neural networks and statistical techniques: A review of applications
Expert Systems with Applications: An International Journal
A new mathematical programming approach to multi-group classification problems
Computers and Operations Research
Experimental comparison of parametric, non-parametric, and hybrid multigroup classification
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
This paper reports the relative performance of an experimental comparison of some well-known classification techniques such as classical statistical, artificial intelligence, mathematical programming (MP), and hybrid approaches. In particular, we examine the four-group, three-variable problem and the associated error rates for the four groups when each of the models is applied to various sets of simulated data. The data had varying characteristics such as multicollinearity, nonlinearity, sample proportions, etc. We concentrate on individual error rates for the four groups, i.e., we count the number of group 1 values classified into group 2, group 3, and group 4 and vice versa. The results indicate that in general not only are MP, k-NN, and hybrid approaches relatively better at overall classification but they also provide a much better balance between error rates for the top customer groups. The results also indicate that the MP and hybrid approaches provide relatively higher and stable classification accuracy under all the data characteristics.