Open Access
ARTICLE
Self-Tuning Parameters for Decision Tree Algorithm Based on Big Data Analytics
Manar Mohamed Hafez1,*, Essam Eldin F. Elfakharany1, Amr A. Abohany2, Mostafa Thabet3
1 College of Computing and Information Technology, Arab Academy for Science, Technology & Maritime Transport,
Cairo, Egypt
2 Faculty of Computers and Information, Kafrelsheikh University, Kafrelsheikh, Egypt
3 Faculty of Computers and Information, Fayoum University, Fayoum, Egypt
* Corresponding Author: Manar Mohamed Hafez. Email:
Computers, Materials & Continua 2023, 75(1), 943-958. https://doi.org/10.32604/cmc.2023.034078
Received 05 July 2022; Accepted 09 November 2022; Issue published 06 February 2023
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.
Keywords
Cite This Article
M. M. Hafez, E. E.F. Elfakharany, A.A. Abohany and M. Thabet, "Self-tuning parameters for decision tree algorithm based on big data analytics,"
Computers, Materials & Continua, vol. 75, no.1, pp. 943–958, 2023.