Predicting Crisis in Global Trade Network: An Enhanced Decision Tree Based Methods
Vashisht Marhwal1, Piyush Bamel2, Tanay Agarwal3
1Vashisht Marhwal, Department of Computer Science and Engineering, Vellore Institute of Technology, Chennai (Tamil Nadu), India.
2Piyush Bamel, Department of Computer Science and Engineering, Vellore Institute of Technology, Chennai (Tamil Nadu), India.
3Tanay Agarwal, Department of Computer Science and Engineering, Vellore Institute of Technology, Chennai (Tamil Nadu), India.
Manuscript received on 16 December 2019 | Revised Manuscript received on 23 December 2019 | Manuscript Published on 31 December 2019 | PP: 314-318 | Volume-9 Issue-1S3 December 2019 | Retrieval Number: A10591291S319/19©BEIESP | DOI: 10.35940/ijeat.A1059.1291S319
Open Access | Editorial and Publishing Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: International Trade Relations represent a natural Social Information Network that has been extensively analyzed for various purposes like monitoring the global economy. The aim is to use the Global Trade Network to predict the occurrence of natural disasters or financial crisis based on the fact that the trade relations tax a hit in their patterns. The Global Network compromises of Export-Import Relations between the countries in the form of a Weighted Social Network. Predicting Trade relations help us effectively predict any future crisis and prepare for the same. An analysis of the Global Trade Network would discuss the centrality measures and Degree strengths. Using a list of crises which has occurred in the past and with the help of an efficient Machine Learning Model and Sampling Technique the aim is to improve the accuracy and precision of our prediction and discuss the implications on the network.
Keywords: Crisis Prediction, Decision Tree, Global Trade Network, Social Network Analysis.
Scope of the Article: Network Security Trust, & Privacy