Abstract
Bibliometrics such as the number of papers and times cited are often used to compare researchers based on specific criteria. The criteria, however, are different in each research domain, and are set by empirical laws. Moreover, there are arguments such that the simple sum of metric values works to the advantage of elders. Therefore, this paper attempts to constitute features from time series data of bibliometrics, and then classify the researchers according to the features. In detail, time series patterns, which correspond to knowledge of bibliometrics, are extracted from the large amount of bibliographic datasets, and then a model to classify whether the researchers are "distinguished" or not is created by machine learning techniques. The experiments achieved an F-measure of 81.0% in the classification of 42 researchers in a research domain based on the datasets of Japan Science and Technology Agency and Elsevier's Scopus. In the future, we will conduct verification on a number of researchers in several domains, and then make use of discovering "distinguished" researchers, who are not widely known so far.
