Author Matching Classification on a Highly Imbalanced Bibliographic Data using Cost-Sensitive Deep Neural Network

firdaus, firdaus (2021) Author Matching Classification on a Highly Imbalanced Bibliographic Data using Cost-Sensitive Deep Neural Network. Proceedings - 3rd International Conference on Informatics, Multimedia, Cyber, and Information System, ICIMCIS 2021. pp. 86-89.

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Abstract

One of the stages before classifying the author matching is to combine the data, in this case the resulting data becomes highly imbalanced dataset, between the author who matches or the author who does not match. This paper presents a method to solve the highly imbalanced problem in author matching classification. The method used Cost-Sensitive Deep Neural Network (CSDNN). CSDNN will consider costs that vary from the type of data misclassification. As text feature similarity measures, we use cosine similarity. And we use Digital Bibliography Library Project (DBLP) data as a dataset. The result is outstanding in terms of specificity 0.99, precision 0.95, recall 0.96, f1-score 0.96, and accuracy 0.99. © 2021 IEEE.

Item Type:	Article
Subjects:	Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation.
Divisions:	09-Faculty of Computer Science > 56201-Computer Systems (S1)
Depositing User:	Mr Firdaus Firdaus
Date Deposited:	17 Mar 2023 15:09
Last Modified:	17 Mar 2023 15:09
URI:	http://repository.unsri.ac.id/id/eprint/90711

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