The class of continuous time Bayesian network classiﬁers is deﬁned; it solves the problem of supervised classiﬁcation on multivariate trajectories evolving in continuous time. The trajectory consists of the values of discrete attributes that are measured in continuous time, while the predicted class is expected to occur in the future. Two instances from this class, namely the continuous time naive Bayes classiﬁer and the continuous time tree augmented naive Bayes classiﬁer, are introduced and analysed. They implement a trade-oﬀ between computational complexity and classiﬁcation accuracy. Learning and inference for the class of continuous time Bayesian network classiﬁers are addressed, in the case where complete data are available. A learning algorithm for the continuous time naive Bayes classiﬁer and an exact inference algorithm for the class of continuous time Bayesian network classiﬁers are described. The performance of the continuous time naive Bayes classiﬁer is assessed in the case where real-time feedback to neurological patients undergoing motor rehabilitation must be provided.
Stella, F.A., & Amer, Y. (2012). Continuous Time Bayesian Network Classiﬁers. JOURNAL OF BIOMEDICAL INFORMATICS.
|Citazione:||Stella, F.A., & Amer, Y. (2012). Continuous Time Bayesian Network Classiﬁers. JOURNAL OF BIOMEDICAL INFORMATICS.|
|Tipo:||Articolo in rivista - Articolo scientifico|
|Carattere della pubblicazione:||Scientifica|
|Presenza di un coautore afferente ad Istituzioni straniere:||No|
|Titolo:||Continuous Time Bayesian Network Classiﬁers|
|Autori:||Stella, FA; Amer, Y|
|Data di pubblicazione:||2012|
|Rivista:||JOURNAL OF BIOMEDICAL INFORMATICS|
|Appare nelle tipologie:||01 - Articolo su rivista|