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

Big data is usually unstructured, and many applications require the analysis in real-time. Decision tree (DT) algorithm widely used to analyze big data. Selecting optimal depth of DT time-consuming process as it requires iterations. In this paper, we have designed a modified version (DT). The aims achieve by self-tuning running parameters improving accuracy. efficiency was verified using two datasets (airport fire datasets). airport dataset has 500000 instances 600000 instances. A comparison been made between standard with results showing that performs better. This conducted on multi-node Apache Spark tool Amazon web services. Resulting accuracy an increase 6.85% for first 8.85% dataset. conclusion, showed better handling different-sized compared algorithm.