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http://dx.doi.org/10.1007/978-1-4614-8283-3_7

has been cited by the following article:

• TITLE: Clustering of the Values of a Response Variable and Simultaneous Covariate Selection Using a Stepwise Algorithm

  AUTHORS: Olivier Collignon, Jean-Marie Monnez

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

  JOURNAL NAME: Applied Mathematics, Vol.7 No.15, September 12, 2016

  ABSTRACT: 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.