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Efficient Bitcoin address classification using quantum-inspired feature selection
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DOI：10.1007/s42484-025-00302-3.png)
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Abstract
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Over 900 million Bitcoin transactions have been recorded, posing considerable challenges for machine learning regarding computation time and maintaining prediction accuracy. We propose an innovative approach using quantum-inspired algorithms implemented with simulated annealing and quantum annealing to address the challenge of local minima in solution spaces. This method efficiently identifies key features linked to mixer addresses, significantly reducing model training time. By categorizing Bitcoin addresses into six classes–exchanges, faucets, gambling, marketplaces, mixers, and mining pools–and applying supervised learning methods, our results demonstrate that feature selection with SA reduced training time by 30.3% compared to using all features in a random forest model while maintaining a 91% F1-score for mixer addresses. This highlights the potential of quantum-inspired algorithms to swiftly and accurately identify high-risk Bitcoin addresses based on transaction features.
Keywords:
Bitcoin
Blockchain
Feature selection
Machine learning
Quantum-inspired acceleration
Journal
Q
IF:
4.4
Papers:
442
Citations:
796
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Cited Papers
ShareSaveShareSaveMixers Detection in bitcoin network: a step towards detecting money laundering in crypto-currencies
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Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
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Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data MiningIF0
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