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Daniel de Roux

dblp:223/3194 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 2018
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 first-authorDatabases, data management, data science and information retrieval · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Databases, data mining, and information retrieval
1 paper
Data mining · 100%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Computational finance and economics · 100%

Topics — the 4 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Data mining
anomaly detection
0.312018
Tax Fraud Detection for Under-Reporting Declarations Using an Unsupervised Machine Learning Approach · KDD 2018
Data mining › anomaly detection
fraud detection
0.312018
Tax Fraud Detection for Under-Reporting Declarations Using an Unsupervised Machine Learning Approach · KDD 2018
Data mining
clustering
0.112018
Tax Fraud Detection for Under-Reporting Declarations Using an Unsupervised Machine Learning Approach · KDD 2018
Data mining › clustering
unsupervised learning
0.112018
Tax Fraud Detection for Under-Reporting Declarations Using an Unsupervised Machine Learning Approach · KDD 2018

Methods — techniques the papers use, named apart from their topics

unsupervised machine learning · 0.7
YearPublicationVenuePosition
2018 Tax Fraud Detection for Under-Reporting Declarations Using an Unsupervised Machine Learning Approach
abstract
Tax fraud is the intentional act of lying on a tax return form with intent to lower one's tax liability. Under-reporting is one of the most common types of tax fraud, it consists in filling a tax return form with a lesser tax base. As a result of this act, fiscal revenues are reduced, undermining public investment.
Daniel de Roux, Boris Perez, Andrés Moreno, María-Del-Pilar Villamil, César Figueroa
KDD1