# The impact of imbalanced training data on machine learning for author name disambiguation

arXiv: https://arxiv.org/abs/1808.00525 · HTML: https://mlsanity.com/paper/1808.00525/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jul 2018.

## Built on by (most cited first, 3 of 3)

- [Key Technology Considerations in Developing and Deploying Machine Learning Models in Clinical Radiology Practice](https://mlsanity.com/paper/2102.01979/) (2021)
- [Effect of forename string on author name disambiguation](https://mlsanity.com/paper/2102.03250/) (2021)
- [Generating automatically labeled data for author name disambiguation: An iterative clustering method](https://mlsanity.com/paper/2102.03272/) (2021)

## Abstract (arXiv)

In supervised machine learning for author name disambiguation, negative training data are often dominantly larger than positive training data. This paper examines how the ratios of negative to positive training data can affect the performance of machine learning algorithms to disambiguate author names in bibliographic records. On multiple labeled datasets, three classifiers - Logistic Regression, Na"ive Bayes, and Random Forest - are trained through representative features such as coauthor names, and title words extracted from the same training data but with various positive-negative training data ratios. Results show that increasing negative training data can improve disambiguation performance but with a few percent of performance gains and sometimes degrade it. Logistic Regression and Na"ive Bayes learn optimal disambiguation models even with a base ratio (1:1) of positive and negative training data. Also, the performance improvement by Random Forest tends to quickly saturate roughly after 1:10 ~ 1:15. These findings imply that contrary to the common practice using all training data, name disambiguation algorithms can be trained using part of negative training data without degrading much disambiguation performance while increasing computational efficiency. This study calls for more attention from author name disambiguation scholars to methods for machine learning from imbalanced data.