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 .

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