A boosted-trees method for name disambiguation

This paper proposes a method for classifying true papers of a set of focal scientists and false papers of homonymous authors in bibliometric research processes. It directly addresses the issue of identifying papers that are not associated (“false”) with a given author. The proposed method has four steps: name and affiliation filtering, similarity score construction, author screening, and boosted trees classification. In this methodological paper we calculate error rates for our technique. Therefore, we needed to ascertain the correct attribution of each paper. To do this we constructed a small dataset of 4,253 papers allegedly belonging to a random sample of 100 authors. We apply the boosted trees algorithm to classify papers of authors with total false rate no higher than 30% (i.e. 3,862 papers of 91 authors). A one-run experiment achieves a testing misclassification error 0.55%, testing recall 99.84%, and testing precision 99.60%. A 50-run experiment shows that the median of testing classification error is 0.78% and mean 0.75%. Among the 90 authors in the testing set (one author only appeared in the training set), the algorithm successfully reduces the false rate to zero for 86 authors and misclassifies just one or two papers for each of the remaining four authors.