Improved Classification of Blockchain Transactions Using Feature Engineering and Ensemble Learning
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...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-12-01
|
| Series: | Future Internet |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1999-5903/14/1/16 |
| _version_ | 1797493924505321472 |
|---|---|
| author | Chandrashekar Jatoth Rishabh Jain Ugo Fiore Subrahmanyam Chatharasupalli |
| author_facet | Chandrashekar Jatoth Rishabh Jain Ugo Fiore Subrahmanyam Chatharasupalli |
| author_sort | Chandrashekar Jatoth |
| collection | DOAJ |
| description | 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%. |
| first_indexed | 2024-03-10T01:26:58Z |
| format | Article |
| id | doaj.art-9d9978e666084b6d9602c85f5cf47d29 |
| institution | Directory Open Access Journal |
| issn | 1999-5903 |
| language | English |
| last_indexed | 2024-03-10T01:26:58Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Future Internet |
| spelling | doaj.art-9d9978e666084b6d9602c85f5cf47d292023-11-23T13:49:27ZengMDPI AGFuture Internet1999-59032021-12-011411610.3390/fi14010016Improved Classification of Blockchain Transactions Using Feature Engineering and Ensemble LearningChandrashekar Jatoth0Rishabh Jain1Ugo Fiore2Subrahmanyam Chatharasupalli3Department of Information Technology, National Institute of Technology Raipur, Raipur 492010, IndiaDepartment of Computer Science & Engineering, National Institute of Technology Hamirpur, Hamirpur 177005, IndiaDepartment of Management and Quantitative Studies, Parthenope University, 80132 Napoli, ItalyUnion Public Service Commission, New Delhi 110069, IndiaAlthough 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%.https://www.mdpi.com/1999-5903/14/1/16machine learningartificial intelligenceensemble learningblockchainperformance metrics |
| spellingShingle | Chandrashekar Jatoth Rishabh Jain Ugo Fiore Subrahmanyam Chatharasupalli Improved Classification of Blockchain Transactions Using Feature Engineering and Ensemble Learning Future Internet machine learning artificial intelligence ensemble learning blockchain performance metrics |
| title | Improved Classification of Blockchain Transactions Using Feature Engineering and Ensemble Learning |
| title_full | Improved Classification of Blockchain Transactions Using Feature Engineering and Ensemble Learning |
| title_fullStr | Improved Classification of Blockchain Transactions Using Feature Engineering and Ensemble Learning |
| title_full_unstemmed | Improved Classification of Blockchain Transactions Using Feature Engineering and Ensemble Learning |
| title_short | Improved Classification of Blockchain Transactions Using Feature Engineering and Ensemble Learning |
| title_sort | improved classification of blockchain transactions using feature engineering and ensemble learning |
| topic | machine learning artificial intelligence ensemble learning blockchain performance metrics |
| url | https://www.mdpi.com/1999-5903/14/1/16 |
| work_keys_str_mv | AT chandrashekarjatoth improvedclassificationofblockchaintransactionsusingfeatureengineeringandensemblelearning AT rishabhjain improvedclassificationofblockchaintransactionsusingfeatureengineeringandensemblelearning AT ugofiore improvedclassificationofblockchaintransactionsusingfeatureengineeringandensemblelearning AT subrahmanyamchatharasupalli improvedclassificationofblockchaintransactionsusingfeatureengineeringandensemblelearning |