SciELO - Scientific Electronic Library Online

 
vol.113 issue9-10Potential of interval partial least square regression in estimating leaf area indexThe potential of South African timber products to reduce the environmental impact of buildings author indexsubject indexarticles search
Home Pagealphabetic serial listing  

Services on Demand

Article

Indicators

Related links

  • On index processCited by Google
  • On index processSimilars in Google

Share


South African Journal of Science

On-line version ISSN 1996-7489
Print version ISSN 0038-2353

Abstract

BREED, Douw G.  and  VERSTER, Tanja. The benefits of segmentation: Evidence from a South African bank and other studies. S. Afr. j. sci. [online]. 2017, vol.113, n.9-10, pp.1-7. ISSN 1996-7489.  http://dx.doi.org/10.17159/sajs.2017/20160345.

We applied different modelling techniques to six data sets from different disciplines in the industry, on which predictive models can be developed, to demonstrate the benefit of segmentation in linear predictive modelling. We compared the model performance achieved on the data sets to the performance of popular non-linear modelling techniques, by first segmenting the data (using unsupervised, semi-supervised, as well as supervised methods) and then fitting a linear modelling technique. A total of eight modelling techniques was compared. We show that there is no one single modelling technique that always outperforms on the data sets. Specifically considering the direct marketing data set from a local South African bank, it is observed that gradient boosting performed the best. Depending on the characteristics of the data set, one technique may outperform another. We also show that segmenting the data benefits the performance of the linear modelling technique in the predictive modelling context on all data sets considered. Specifically, of the three segmentation methods considered, the semi-supervised segmentation appears the most promising. SIGNIFICANCE: • The use of non-linear modelling techniques may not necessarily increase model performance when data sets are first segmented. • No single modelling technique always performed the best. • Applications of predictive modelling are unlimited; some examples of areas of application include database marketing applications; financial risk management models; fraud detection methods; medical and environmental predictive models.

Keywords : predictive models; case studies; logistic regression; linear modelling; semi-supervised segmentation.

        · text in English     · English ( pdf )

 

Creative Commons License All the contents of this journal, except where otherwise noted, is licensed under a Creative Commons Attribution License