Data Cleaning and Machine Learning: A Systematic Literature Review
Pierre-Olivier Côté Amin Nikanjam Nafisa Ahmed Dmytro Humeniuk Foutse Khomh
Context: Machine Learning (ML) is integrated into a growing number of systems
for various applications. Because the performance of an ML model is highly
dependent on the quality of the data it has been trained on, there is a growing
interest in approaches to detect and repair data errors (i.e., data cleaning).
Researchers are also exploring how ML can be used for data cleaning; hence
creating a dual relationship between ML and data cleaning. To the best of our
knowledge, there is no study that comprehensively reviews this relationship.
Objective: This paper's objectives are twofold. First, it aims to summarize the
latest approaches for data cleaning for ML and ML for data cleaning. Second, it
provides future work recommendations. Method: We conduct a systematic
literature review of the papers published between 2016 and 2022 inclusively. We
identify different types of data cleaning activities with and for ML: feature
cleaning, label cleaning, entity matching, outlier detection, imputation, and
holistic data cleaning. Results: We summarize the content of 101 papers
covering various data cleaning activities and provide 24 future work
recommendations. Our review highlights many promising data cleaning techniques
that can be further extended. Conclusion: We believe that our review of the
literature will help the community develop better approaches to clean data.