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

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DOI: 10.4236/am.2016.715141    1,026 Downloads   1,465 Views Citations

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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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.

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