Open AccessData Mining Generating Decision Trees to Alert System Against Death and Losses in Egg Production
Author(s) -
Mário Mollo Neto,
Maria Elena Silva Montanhani,
Leda Gobbo de Freitas Bueno,
Érik dos Santos Harada,
Danilo Florentino Pereira
Publication year - 2020
Language(s) - English
Resource type - Journals
ISSN - 2411-2933
DOI - 10.31686/ijier.vol8.iss8.2584
Subject(s) - c4.5 algorithm , decision tree , production (economics) , computer science , heat stress , decision system , database , operations research , data mining , machine learning , mathematics , biology , zoology , macroeconomics , support vector machine , economics , naive bayes classifier
Climatic changes and high temperatures have been affecting animal production and the well-being of laying birds, with heat stress and high mortality rates, generating economic losses. Legacy databases can contain information to help model thermal comfort at climatic extremes. They can enable decision trees to be created through the use of data mining to prevent mortality and production losses. Thus, the objective of this study is to seek to develop decision trees, for application as an alert system, for the incidence of caloric stress in the production of layers. We used a database of three aviaries located in the city of Bastos-SP, collected in 2013. The data were organized in Excel® spreadsheets, and processed with the Weka® software with the J48 (C4.5) algorithm for mining of the data. The technique allowed the construction of decision trees that in the chosen sheds were classified with respectively 99.73%, 99.61%, and 98.71% of correct answers and with Kappa indexes equal to 0.9958, 0.9907 and 0.9663, which indicate that the three classifiers built are excellent. Thus, the proposed system, with the decision trees built, can serve as a basis for the construction of an alert system to be applied to the three warehouses simultaneously.