The impact of imbalanced training data on machine learning for author name disambiguation
This study addresses the problem of computational inefficiency in author name disambiguation for bibliographic scholars, but it is incremental as it builds on existing methods without introducing new techniques.
This paper investigates how imbalanced training data ratios affect machine learning performance for author name disambiguation, finding that increasing negative data yields small performance gains (a few percent) or degradation, with optimal models achievable at balanced ratios and saturation after certain thresholds.
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, Naïve 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 Naïve 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.