Interval-Valued Feature Selection
Cecilio Ángulo, Francisco Javier Ruiz Vegas, Andreu Catalá, Albert Samà · 2011
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 .