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

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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Applied Mathematics, Scientific Research Publishing, 2016, 7 (15), pp.1639-1648. 〈10.4236/am.2016.715141〉
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Dernière modification le : vendredi 5 octobre 2018 - 15:13:54
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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, 7 (15), pp.1639-1648. 〈10.4236/am.2016.715141〉. 〈hal-01395535〉

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