Clustering of the values of a response variable and simultaneous covariate selection using a stepwise algorithm

Olivier Collignon 1, 2 Jean-Marie Monnez 2, 3
2 Probabilités et statistiques
IECL - Institut Élie Cartan de Lorraine
3 BIGS - Biology, genetics and statistics
Inria Nancy - Grand Est, IECL - Institut Élie Cartan de Lorraine
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.
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Article dans une revue
Applied Mathematics, Scientific Research Publishing, 2016, 07, pp.1639 - 1648. 〈10.4236/am.2016.715141〉
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Dernière modification le : jeudi 11 janvier 2018 - 06:26:22
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Olivier Collignon, Jean-Marie Monnez. Clustering of the values of a response variable and simultaneous covariate selection using a stepwise algorithm. Applied Mathematics, Scientific Research Publishing, 2016, 07, pp.1639 - 1648. 〈10.4236/am.2016.715141〉. 〈hal-01395535〉

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