A bitcoin service community classification method based on Random Forest and improved KNN algorithm
Abstract There are service communities with different functions in the Bitcoin transactions system. Identifying community categories helps to further understand the Bitcoin transactions system and facilitates targeted regulation of anonymized Bitcoin transactions. To this end, a Bitcoin service comm...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-09-01
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| Series: | IET Blockchain |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/blc2.12064 |
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| _version_ | 1846163456481296384 |
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| author | Muyun Gao Shenwen Lin Xin Tian Xi He Ketai He Shifeng Chen |
| author_facet | Muyun Gao Shenwen Lin Xin Tian Xi He Ketai He Shifeng Chen |
| author_sort | Muyun Gao |
| collection | DOAJ |
| description | Abstract There are service communities with different functions in the Bitcoin transactions system. Identifying community categories helps to further understand the Bitcoin transactions system and facilitates targeted regulation of anonymized Bitcoin transactions. To this end, a Bitcoin service community classification method based on Random Forest and improved K‐Nearest Neighbor (KNN) algorithm is proposed. First, the transaction characteristics of different types of communities are analyzed and summarized, and the corresponding transaction features are extracted from the address and entity levels; then multiple classification algorithms are compared, the optimal model to filter the effective features is selected, and the feature vector of entity addresses is constructed. Finally, a classification model is constructed based on Random Forest and improved KNN algorithm to classify the entities. By constructing different classification models for experimental comparison, the accuracy and stability advantages of the proposed method for classification in service community classification research are verified. |
| format | Article |
| id | doaj-art-e1c0b2d2372b4090a9f84a47a2e9dd36 |
| institution | Kabale University |
| issn | 2634-1573 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Blockchain |
| spelling | doaj-art-e1c0b2d2372b4090a9f84a47a2e9dd362024-11-19T09:25:56ZengWileyIET Blockchain2634-15732024-09-014327628610.1049/blc2.12064A bitcoin service community classification method based on Random Forest and improved KNN algorithmMuyun Gao0Shenwen Lin1Xin Tian2Xi He3Ketai He4Shifeng Chen5School of Mechanical Engineering University of Science and Technology Beijing Beijing ChinaInternet Financial Security Technology Key Laboratory National Computer Network Emergency Response Technical Team/Coordination Center of China Beijing ChinaSchool of Mechanical Engineering University of Science and Technology Beijing Beijing ChinaSchool of Mechanical Engineering University of Science and Technology Beijing Beijing ChinaSchool of Mechanical Engineering University of Science and Technology Beijing Beijing ChinaSchool of Mechanical Engineering University of Science and Technology Beijing Beijing ChinaAbstract There are service communities with different functions in the Bitcoin transactions system. Identifying community categories helps to further understand the Bitcoin transactions system and facilitates targeted regulation of anonymized Bitcoin transactions. To this end, a Bitcoin service community classification method based on Random Forest and improved K‐Nearest Neighbor (KNN) algorithm is proposed. First, the transaction characteristics of different types of communities are analyzed and summarized, and the corresponding transaction features are extracted from the address and entity levels; then multiple classification algorithms are compared, the optimal model to filter the effective features is selected, and the feature vector of entity addresses is constructed. Finally, a classification model is constructed based on Random Forest and improved KNN algorithm to classify the entities. By constructing different classification models for experimental comparison, the accuracy and stability advantages of the proposed method for classification in service community classification research are verified.https://doi.org/10.1049/blc2.12064Bitcoincommunity entity classificationfeature filteringimproved KNN algorithmRandom Forest algorithm |
| spellingShingle | Muyun Gao Shenwen Lin Xin Tian Xi He Ketai He Shifeng Chen A bitcoin service community classification method based on Random Forest and improved KNN algorithm IET Blockchain Bitcoin community entity classification feature filtering improved KNN algorithm Random Forest algorithm |
| title | A bitcoin service community classification method based on Random Forest and improved KNN algorithm |
| title_full | A bitcoin service community classification method based on Random Forest and improved KNN algorithm |
| title_fullStr | A bitcoin service community classification method based on Random Forest and improved KNN algorithm |
| title_full_unstemmed | A bitcoin service community classification method based on Random Forest and improved KNN algorithm |
| title_short | A bitcoin service community classification method based on Random Forest and improved KNN algorithm |
| title_sort | bitcoin service community classification method based on random forest and improved knn algorithm |
| topic | Bitcoin community entity classification feature filtering improved KNN algorithm Random Forest algorithm |
| url | https://doi.org/10.1049/blc2.12064 |
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