Mislabel Detection of Finnish Publication Ranks

TL;DR

This paper applies an Extreme Learning Machine (ELM) approach to detect mislabels in Finnish academic publication rankings, aiming to improve accuracy and reliability of publication channel assessments.

Contribution

It introduces and tests a novel ELM-based mislabel detection method with comprehensive feature characterization for publication channels.

Findings

ELM-based approach effectively detects mislabels in publication ranks.

Comparison shows improved accuracy over reference methods.

The method identifies specific misclassified publication channels.

Abstract

The paper proposes to analyze a data set of Finnish ranks of academic publication channels with Extreme Learning Machine (ELM). The purpose is to introduce and test recently proposed ELM-based mislabel detection approach with a rich set of features characterizing a publication channel. We will compare the architecture, accuracy, and, especially, the set of detected mislabels of the ELM-based approach to the corresponding reference results on the reference paper.