Self-Tuning Parameters for Decision Tree Algorithm Based on Big Data Analytics  

作　　者：Manar Mohamed Hafez Essam Eldin F.Elfakharany Amr A.Abohany Mostafa Thabet 

机构地区：[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

出　　处：《Computers, Materials & Continua》2023年第4期943-958,共16页计算机、材料和连续体（英文）

摘　　要：Big data is usually unstructured, and many applications require theanalysis in real-time. Decision tree (DT) algorithm is widely used to analyzebig data. Selecting the optimal depth of DT is time-consuming process as itrequires many iterations. In this paper, we have designed a modified versionof a (DT). The tree aims to achieve optimal depth by self-tuning runningparameters and improving the accuracy. The efficiency of the modified (DT)was verified using two datasets (airport and fire datasets). The airport datasethas 500000 instances and the fire dataset has 600000 instances. A comparisonhas been made between the modified (DT) and standard (DT) with resultsshowing that the modified performs better. This comparison was conductedon multi-node on Apache Spark tool using Amazon web services. Resultingin accuracy with an increase of 6.85% for the first dataset and 8.85% for theairport dataset. In conclusion, the modified DT showed better accuracy inhandling different-sized datasets compared to standard DT algorithm.

关 键 词：Big data classification decision tree Amazon web services 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论]