TY - JOUR

T1 - A boosted-trees method for name disambiguation

AU - Wang, Jian

AU - Berzins, Kaspars

AU - Hicks, Diana

AU - Melkers, Julia

AU - Xiao, Fang

AU - Pinheiro, Diogo

PY - 2012/11/30

Y1 - 2012/11/30

N2 - 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.

AB - 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.

KW - Boosted trees

KW - Classification tree

KW - Common names

KW - Name disambiguation

U2 - 10.1007/s11192-012-0681-1

DO - 10.1007/s11192-012-0681-1

M3 - Journal article

AN - SCOPUS:84867987040

VL - 93

SP - 391

EP - 411

JO - Scientometrics

JF - Scientometrics

SN - 0138-9130

IS - 2

ER -