Improved Classification of Blockchain Transactions Using Feature Engineering and Ensemble Learning

Chandrashekar Jatoth, Rishabh Jain, Ugo Fiore and Subrahmanyam Chatharasupalli
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Chandrashekar Jatoth: Department of Information Technology, National Institute of Technology Raipur, Raipur 492010, India
Rishabh Jain: Department of Computer Science & Engineering, National Institute of Technology Hamirpur, Hamirpur 177005, India
Ugo Fiore: Department of Management and Quantitative Studies, Parthenope University, 80132 Napoli, Italy
Subrahmanyam Chatharasupalli: Union Public Service Commission, New Delhi 110069, India

Future Internet, 2021, vol. 14, issue 1, 1-12

Abstract: Although the blockchain technology is gaining a widespread adoption across multiple sectors, its most popular application is in cryptocurrency. The decentralized and anonymous nature of transactions in a cryptocurrency blockchain has attracted a multitude of participants, and now significant amounts of money are being exchanged by the day. This raises the need of analyzing the blockchain to discover information related to the nature of participants in transactions. This study focuses on the identification for risky and non-risky blocks in a blockchain. In this paper, the proposed approach is to use ensemble learning with or without feature selection using correlation-based feature selection. Ensemble learning yielded good results in the experiments, but class-wise analysis reveals that ensemble learning with feature selection improves even further. After training Machine Learning classifiers on the dataset, we observe an improvement in accuracy of 2–3% and in F-score of 7–8%.

Keywords: machine learning; artificial intelligence; ensemble learning; blockchain; performance metrics (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2021
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