Single and Multi-objective Genetic Programming Methods for Prediction Intervals

Karina Brotto Rebuli, Mario Giacobini, Niccolò Tallone, Leonardo Vanneschi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

A PI is the range of values in which the real target value of a supervised learning task is expected to fall into, and it should combine two contrasting properties: to be as narrow as possible, and to include as many data observations as possible. This article presents an study on modelling Prediction Intervals (PI) with two Genetic Programming (GP) methods. The first proposed GP method is called CWC-GP, and it evolves simultaneously the lower and upper boundaries of the PI using a single fitness measure. This measure is the Coverage Width-based Criterion (CWC), which combines the width and the probability coverage of the PI. The second proposed GP method is called LUBE-GP, and it evolves independently the lower and upper boundaries of the PI. This method applies a multi-objective approach, in which one fitness aims to minimise the width and the other aims to maximise the probability coverage of the PI. Both methods were applied with the Direct and the Sequential approaches. In the former, the PI is assessed without the crisp prediction of the model. In the latter, the method makes use of the crisp prediction to find the PI boundaries. The proposed methods showed to have good potential on assessing PIs and the results pave the way to further investigations.
Original languageEnglish
Title of host publicationArtificial Life and Evolutionary Computation
Subtitle of host publication16th Italian Workshop, WIVACE 2022, Gaeta, Italy, September 14–16, 2022, Revised Selected Papers
EditorsClaudio de Stefano, Francesco Fontanella, Leonardo Vanneschi
Place of Publication Gewerbestrasse, Cham, Switzerland
PublisherSpringer, Cham
Chapter17
Pages205-218
Number of pages14
ISBN (Electronic)978-3-031-31183-3
ISBN (Print)978-3-031-31182-6
DOIs
Publication statusPublished - 30 Apr 2023
EventXVI International Workshop on Artificial Life and Evolutionary Computation - Gaeta, Italy
Duration: 14 Sept 202216 Sept 2022
Conference number: 14
http://wivace2022.unicas.it/

Publication series

NameCommunications in Computer and Information Science
Volume1780
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937

Conference

ConferenceXVI International Workshop on Artificial Life and Evolutionary Computation
Abbreviated titleWIVACE 2022
Country/TerritoryItaly
CityGaeta
Period14/09/2216/09/22
Internet address

Keywords

  • Prediction Interval
  • Crisp prediction
  • Modelling uncertainty
  • Machine Learning

Fingerprint

Dive into the research topics of 'Single and Multi-objective Genetic Programming Methods for Prediction Intervals'. Together they form a unique fingerprint.

Cite this