Abstract
We show that induction graphs can be generalized to treat more general prediction problems than those usually treated: prediction of a class variable or of a one dimensional continuous variable. We treat here the case in which the prediction concerns a multivariate continuous response. The approach used, called here GENIND1, is a combination of previous work by two of the authors (RECPAM and SIPINA). We show also that in the GENIND1 framework, clustering (unsupervised learning) as well as prediction (supervised learning) can be treated. The approach is applied to nutritional data.
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Ciampi, A., Zighed, D.A., Clech, J. (2000). Trees and Induction Graphs for Multivariate Response. In: Zighed, D.A., Komorowski, J., Żytkow, J. (eds) Principles of Data Mining and Knowledge Discovery. PKDD 2000. Lecture Notes in Computer Science(), vol 1910. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45372-5_37
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DOI: https://doi.org/10.1007/3-540-45372-5_37
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