Interval-valued feature selection
Document typeConference report
Rights accessOpen Access
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 .