Author(s)

Olivier Collignon¹, Jean-Marie Monnez^2,3,4

¹Luxembourg Institute of Health, Strassen, Luxembourg.
²Institut Elie Cartan de Lorraine, University de Lorraine, CNRS UMR 7502, Vandoeuvre-lès-Nancy, France.
³INRIA, Projet BIGS, Vandoeuvre-lès-Nancy, France.
⁴CIC-P, CHRU, University Hospital, Nancy, France.

In supervised learning the number of values of a response variable can be very high. Grouping these values in a few clusters can be useful to perform accurate supervised classification analyses. On the other hand selecting relevant covariates is a crucial step to build robust and efficient prediction models. We propose in this paper an algorithm that simultaneously groups the values of a response variable into a limited number of clusters and selects stepwise the best covariates that discriminate this clustering. These objectives are achieved by alternate optimization of a user-defined model selection criterion. This process extends a former version of the algorithm to a more general framework. Moreover possible further developments are discussed in detail.

Classification, Variable Selection, Supervised Learning, Akaike Information Criterion, Wilks’ Lambda

Collignon, O. and Monnez, J. (2016) Clustering of the Values of a Response Variable and Simultaneous Covariate Selection Using a Stepwise Algorithm. Applied Mathematics, 7, 1639-1648. doi: 10.4236/am.2016.715141.