This paper presents an important development of a novel non-parametric object classification technique, namely CaRBS (Classification and Ranking Belief Simplex), to enable regression-type analyses. Termed RCaRBS, it is, as with CaRBS, an evidence-based technique, with its mathematical operations based on the Dempster-Shafer theory of evidence. Its exposition is demonstrated here by modelling the strategic fit of a set of public organizations. In addition to the consideration of the predictive fit of a series of models, graphical exploration of the contribution of individual variables in the derived models is also undertaken when using RCaRBS. Comparison analyses, including through fivefold cross-validation, are carried out using multiple regression and neural networks models. The findings highlight that RCaRBS achieves parity of test set predictive fit with regression and better fit than neural networks. The RCaRBS technique can also enable researchers to explore non-linear relationships (contributions) between variables in greater detail than either regression or neural networks models.
- Decision analysis
- Evidence theory
- Neural networks
- Strategic fit
- Trigonometric differential evolution