In this paper we introduce the use of interval variables
in classification problems of time series
signals. By introducing the concept of interval kernel
as a similarity measure among intervals, modifications
for some well-known feature selection
methods are developed in order to apply these methods
to select the most relevant interval variables.
A comparison against standard point attributes feature
selection (Relief and FSDD) is made for purposes
of validation .Peer ReviewedPreprin
