Naslov
Peach firmness prediction using optimized regression trees models

Autori
Ivanovski, Tomislav ; Zhang, Xiaoshuan ; Jemrić, Tomislav ; Gulić, Marko ; Matetić, Maja

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
Proceedings of the 33rd International DAAAM Virtual Symposium "Intelligent Manufacturing & Automation" / Katalinić, Branko - : DAAAM International Vienna, 2022, 0480-0489

ISBN
978-3-902734-36-5

Skup
33rd DAAAM International Symposium

Mjesto i datum
Beč, Austrija; online, 27.10.2022. - 28.10.2022

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
Peach firmness prediction ; regression trees ; global optimization ; machine learning ; metaheuristics

Sažetak
The paper focuses on creating accurate model for peach firmness prediction. For this purpose, multiple machine learning models and their optimized variations are developed and compared. Because of its simplicity and robustness, multiple linear regression is used as a base-line model for predicting peach firmness. It assumes a linear relationship between numerical predictors and the outcome. Regression trees is the second developed model. It is a flexible data-driven model that can be used for predicting numerical outcome. The experiment aims to investigate the possibility of improving regression tree model using various metaheuristic optimization techniques implemented in metaheuristicOpt and GA R packages. As a proof of concept, prediction accuracy between multiple linear regression, regression trees and optimized regression trees models is compared. The results show that it is possible to improve the peach firmness prediction accuracy of regression trees model using metaheuristic algorithms.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo, Poljoprivreda (agronomija), Informacijske i komunikacijske znanosti

POVEZANOST RADA