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

Autor/inn/en	Kim, Jinseok; Kim, Jenna
Titel	The impact of imbalanced training data on machine learning for author name disambiguation.
Quelle	In: Scientometrics, (2018) 1, S.511-526 PDF als Volltext Verfügbarkeit
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0138-9130
DOI	10.1007/s11192-018-2865-9
Schlagwörter	Author name disambiguation; Negative training data; Imbalanced training data; Supervised machine learning
Abstract	Abstract 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-to-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 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.
Erfasst von	OLC
Update	2023/2/05