Discriminant analysis of distributional data via fractional programming

Sónia Dias, Paula Brito, Paula Amaral

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

We address classification of distributional data, where units are described by histogram or interval-valued variables. The proposed approach uses a linear discriminant function where distributions or intervals are represented by quantile functions, under specific assumptions. This discriminant function allows defining a score for each unit, in the form of a quantile function, which is used to classify the units in two a priori groups, using the Mallows distance. There is a diversity of application areas for the proposed linear discriminant method. In this work we classify the airline companies operating in NY airports based on air time and arrival/departure delays, using a full year flights.

Original languageEnglish
Pages (from-to)206-218
JournalEuropean Journal of Operational Research
Volume294
Issue number1
DOIs
Publication statusPublished - 1 Oct 2021

Keywords

  • Classification
  • Data science
  • Histogram data
  • Multivariate statistics
  • Symbolic data analysis

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