A Proposal for Outlier and Noise Detection in Public Officials' Affidavits

TL;DR

This paper explores hybrid outlier and noise detection methods to improve the quality and integrity of public officials' affidavits in Argentina, aiming to identify anomalies and enhance data reliability.

Contribution

It introduces a novel approach combining multiple detection techniques tailored for public affidavit data, with validation on real-world Argentine datasets.

Findings

Effective detection of anomalies in affidavit data

Improved data quality and reliability

Potential civic applications for public data validation

Abstract

Outlier and noise detection processes are highly useful in the quality assessment of any kind of database. Such processes may have novel civic and public applications in the detection of anomalies in public data. The purpose of this work is to explore the possibilities of experimentation with, validation and application of hybrid outlier and noise detection procedures in public officials' affidavit systems currently available in Argentina.