2020/11/30 by Javier Fumanal-Idocin, Zdenko Takáč, Javier Fernández Jose Antonio Sanz +4 · 1 citation
Computer Science · Mathematics · #cs.CV #cs.HC #cs.NA #math.NA
paper · pdf · doi:10.1109/tfuzz.2021.3092824
arxiv created 2021/07/01 · arxiv updated 2021/07/02
In this work we study the use of moderate deviation functions to measure similarity and dissimilarity among a set of given interval-valued data. To do so, we introduce the notion of interval-valued moderate deviation function and we study in particular those interval-valued moderate deviation functions which preserve the width of the input intervals. Then, we study how to apply these functions to construct interval-valued aggregation functions. We have applied them in the decision making phase of two Motor-Imagery Brain Computer Interface frameworks, obtaining better results than those obtained using other numerical and intervalar aggregations.