scholarly journals Characterizing key agents in the cryptocurrency economy through blockchain transaction analysis

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Xiao Fan Liu ◽  
Huan-Huan Ren ◽  
Si-Hao Liu ◽  
Xian-Jian Jiang
Keyword(s):  
Machine Learning ◽  
Trading Volume ◽  
Economic Behavior ◽  
Behavior Patterns ◽  
Learning Models ◽  
Economic Agents ◽  
Interpretable Machine Learning ◽  
Transaction Analysis ◽  
Almost All ◽  
Machine Learning Models

AbstractThe cryptocurrency economy provides a comprehensive digital trace of human economic behavior: almost all cryptocurrency users’ activities are faithfully recorded in transactions on public blockchains. However, the user identifiers in the transaction records, i.e., blockchain addresses, are anonymous. That is, they cannot be associated with any real “off-chain” identify of actual users. Nonetheless, identifying the economic roles of the addresses from their past behaviors is still feasible. This paper analyzes Ethereum token transactions, characterizes key economic agents’ behavior from their transaction patterns, and explores their identifiability through interpretable machine learning models. Specifically, six types of most active economic agents are considered, including centralized cryptocurrency exchanges, decentralized exchanges, cryptocurrency wallets, token issuers, airdrop services, and gaming services. Transaction patterns such as trading volume, transaction tempo, and structural properties of transaction networks are defined for individual blockchain addresses. The results showed that cryptocurrency exchanges and online wallets have signature behavior patterns and hence can be accurately distinguished from other agents. Token issuers, airdrop services, and gaming services can sometimes be confused. Moreover, transaction networks’ features provide the richest information in the economic agent’s identification.

Application of interpretable machine learning models for the intelligent decision

2019 ◽  
Vol 333 ◽  
pp. 273-283 ◽  
Author(s):  
Yawen Li ◽  
Liu Yang ◽  
Bohan Yang ◽  
Ning Wang ◽  
Tian Wu
Keyword(s):  
Machine Learning ◽  
Learning Models ◽  
Interpretable Machine Learning ◽  
Intelligent Decision ◽  
Machine Learning Models

scholarly journals An Introduction on Interpretable Machine Learning

2020 ◽  
Vol 9 (7S) ◽  
pp. 107-111
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Human Life ◽  
Accuracy Score ◽  
Learning Models ◽  
Ethical Practices ◽  
Interpretable Machine Learning ◽  
Machine Learning Model ◽  
Machine Learning Applications ◽  
Machine Learning Models

As Artificial Intelligence penetrates all aspects of human life, more and more questions about ethical practices and fair uses arise, which has motivated the research community to look inside and develop methods to interpret these Artificial Intelligence/Machine Learning models. This concept of interpretability can not only help with the ethical questions but also can provide various insights into the working of these machine learning models, which will become crucial in trust-building and understanding how a model makes decisions. Furthermore, in many machine learning applications, the feature of interpretability is the primary value that they offer. However, in practice, many developers select models based on the accuracy score and disregarding the level of interpretability of that model, which can be chaotic as predictions by many high accuracy models are not easily explainable. In this paper, we introduce the concept of Machine Learning Model Interpretability, Interpretable Machine learning, and the methods used for interpretation and explanations.

scholarly journals Toward Interpretable Machine Learning Models for Materials Discovery

2019 ◽  
Vol 1 (8) ◽  
pp. 1900045 ◽  
Author(s):  
Paulius Mikulskis ◽  
Morgan R. Alexander ◽  
David Alan Winkler
Keyword(s):  
Machine Learning ◽  
Learning Models ◽  
Interpretable Machine Learning ◽  
Machine Learning Models

Interpretable machine learning with reject option

2018 ◽  
Vol 66 (4) ◽  
pp. 283-290 ◽  
Author(s):  
Johannes Brinkrolf ◽  
Barbara Hammer
Keyword(s):  
Machine Learning ◽  
Vector Quantization ◽  
Random Forests ◽  
Black Box ◽  
Learning Models ◽  
Process Automation ◽  
Reject Option ◽  
Interpretable Machine Learning ◽  
Adversarial Examples ◽  
Machine Learning Models

Abstract Classification by means of machine learning models constitutes one relevant technology in process automation and predictive maintenance. However, common techniques such as deep networks or random forests suffer from their black box characteristics and possible adversarial examples. In this contribution, we give an overview about a popular alternative technology from machine learning, namely modern variants of learning vector quantization, which, due to their combined discriminative and generative nature, incorporate interpretability and the possibility of explicit reject options for irregular samples. We give an explicit bound on minimum changes required for a change of the classification in case of LVQ networks with reject option, and we demonstrate the efficiency of reject options in two examples.

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