TY - JOUR
T1 - Optimization of Atmospheric Plasma Surface Modification Process Using Decision Trees
AU - RadhiaAbd Jeli
JO - Journal of Information and Computing Science
VL - 4
SP - 266
EP - 271
PY - 2024
DA - 2024/01
SN - 14
DO - http://doi.org/
UR - https://global-sci.org/intro/article_detail/jics/22402.html
KW - Atmospheric plasma process, polypropylene, optimization, decision trees 

AB - 1 Textile Material and Processes Research Unit, University of Monastir, Tunisia  
(Received October 11 2019, accepted November 28 2019) 
Decisions trees are one of the most commonly used data mining techniques to practically solve 
classification and prediction problems. They have tree shaped structures in which construction of trees is simple 
and unlike the logistic regression models, decision tree results can be easily understood by the users. In this 
study, a  decision tree induction algorithm known as CART (Classification and Regression Trees) has been 
employed in order to better understand the influence of plasma parameters adjustment on polypropylene (PP) 
film’s hydrophilic surface properties. The cross-validation method was used for pruning the decision tree. The 
root mean square errors (RMSE) and correlation coefficients (R) for training and test subsets were used in 
order  to  get  the  best  fitting model.  The  obtained  decision  tree  regression  model  showed  excellent  learning 
performance and achieved good predictive accuracy.