Mislabel Detection of Finnish Publication Ranks
This work addresses data quality issues in academic ranking systems for researchers and institutions, but it appears incremental as it applies an existing method to a specific dataset.
The paper tackled the problem of detecting mislabeled ranks in Finnish academic publication channels using an Extreme Learning Machine (ELM) approach, comparing its architecture, accuracy, and mislabel detection results to a reference paper.
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.