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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

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Ethnicity-based name partitioning for author name disambiguation using supervised machine learning.

Jinseok Kim¹, Jenna Kim², Jason Owen-Smith³

¹Institute for Research on Innovation & Science, Survey Research Center, Institute for Social Research University of Michigan Ann Arbor Michigan USA.

Journal of the Association for Information Science and Technology

|August 20, 2021

Summary

This summary is machine-generated.

Partitioning author names by ethnicity significantly improves disambiguation accuracy. Tailoring machine learning models to specific ethnic name groups enhances performance, outperforming general approaches.

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Area of Science:

Bibliometrics
Computer Science
Information Science

Background:

Author name disambiguation is crucial for accurate scholarly record-keeping.
Existing methods face challenges, with certain ethnic name groups (e.g., East Asian) proving more difficult to disambiguate.

Purpose of the Study:

To investigate the impact of ethnicity-based name partitioning on author name disambiguation performance.
To determine if specialized models for ethnic name groups outperform general models.

Main Methods:

Four machine learning algorithms were employed.
Algorithms were trained and tested on both the entire dataset and on ethnicity-specific subsets.
Performance was evaluated across different ethnic name groups and dataset configurations.

Main Results:

Ethnicity-based partitioning substantially improved disambiguation performance across all tested ethnic name groups.
Individual models trained on specific ethnic name groups were better suited for their respective data.
Performance gains in correctly identifying matched name pairs exceeded losses in identifying non-matched pairs.
Feature similarities, such as coauthor names, varied significantly across ethnic name groups.

Conclusions:

Grouping author names by ethnicity before disambiguation offers a significant performance enhancement.
Differences in feature similarities across ethnic groups suggest potential for developing ethnicity-specific feature weights.
This approach is effective regardless of natural data distribution or controlled ambiguous name sizes.