Data Cleaning and Machine Learning: A Systematic Literature Review

1 code implementation • 3 Oct 2023 • Pierre-Olivier Côté, Amin Nikanjam, Nafisa Ahmed, Dmytro Humeniuk, Foutse khomh

First, it aims to summarize the latest approaches for data cleaning for ML and ML for data cleaning.

Imputation Outlier Detection

Quality Issues in Machine Learning Software Systems

1 code implementation • 26 Jun 2023 • Pierre-Olivier Côté, Amin Nikanjam, Rached Bouchoucha, Ilan Basta, Mouna Abidi, Foutse khomh

We validate the identified quality issues via a survey with ML practitioners.

Can Ensembling Pre-processing Algorithms Lead to Better Machine Learning Fairness?

no code implementations • 5 Dec 2022 • Khaled Badran, Pierre-Olivier Côté, Amanda Kolopanis, Rached Bouchoucha, Antonio Collante, Diego Elias Costa, Emad Shihab, Foutse khomh

As machine learning (ML) systems get adopted in more critical areas, it has become increasingly crucial to address the bias that could occur in these systems.

Fairness

Quality issues in Machine Learning Software Systems

1 code implementation • 18 Aug 2022 • Pierre-Olivier Côté, Amin Nikanjam, Rached Bouchoucha, Foutse khomh

This empirical study aims to identify a catalog of bad-practices related to poor quality in MLSSs.