  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.
