@Article{cmc.2023.034078,
AUTHOR = {Manar Mohamed Hafez, Essam Eldin F. Elfakharany, Amr A. Abohany, Mostafa Thabet},
TITLE = {Self-Tuning Parameters for Decision Tree Algorithm Based on Big Data Analytics},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {75},
YEAR = {2023},
NUMBER = {1},
PAGES = {943--958},
URL = {http://www.techscience.com/cmc/v75n1/51545},
ISSN = {1546-2226},
ABSTRACT = {Big data is usually unstructured, and many applications require the
analysis in real-time. Decision tree (DT) algorithm is widely used to analyze
big data. Selecting the optimal depth of DT is time-consuming process as it
requires many iterations. In this paper, we have designed a modified version
of a (DT). The tree aims to achieve optimal depth by self-tuning running
parameters and improving the accuracy. The efficiency of the modified (DT)
was verified using two datasets (airport and fire datasets). The airport dataset
has 500000 instances and the fire dataset has 600000 instances. A comparison
has been made between the modified (DT) and standard (DT) with results
showing that the modified performs better. This comparison was conducted
on multi-node on Apache Spark tool using Amazon web services. Resulting
in accuracy with an increase of 6.85% for the first dataset and 8.85% for the
airport dataset. In conclusion, the modified DT showed better accuracy in
handling different-sized datasets compared to standard DT algorithm.},
DOI = {10.32604/cmc.2023.034078}
}