his paper deals with the problem of dimension reduction in the general context of supervised statistical learning, with particular attention to data mining applications. The main goal of the proposed methodology is to improve tree based methods as prediction tool by introducing an alternative approach to data partitioning which is meant to handle large numbers of (possibly correlated) covariates. The key idea is to use suitable combinations of covariates recursively identified.
Canonical variates for Recursive Partitioning in DataMining" / Cappelli, Carmela; C., Conversano. - STAMPA. - (2002), pp. 213-218. (Intervento presentato al convegno COMPSTAT 2002 tenutosi a Berlino nel 24-28 agosto).
Canonical variates for Recursive Partitioning in DataMining"
CAPPELLI, CARMELA;
2002
Abstract
his paper deals with the problem of dimension reduction in the general context of supervised statistical learning, with particular attention to data mining applications. The main goal of the proposed methodology is to improve tree based methods as prediction tool by introducing an alternative approach to data partitioning which is meant to handle large numbers of (possibly correlated) covariates. The key idea is to use suitable combinations of covariates recursively identified.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
