Unbiased machine learning-assisted approach for conditional discretization of human performances
Thepparit Banditwattanawong, Masawee Masdisornchote
TL;DR
This paper introduces new machine learning methods to fairly categorize human performance rankings under specific conditions.
Contribution
Proposes four novel methods for conditional performance discretization using machine learning and a heuristic approach.
Findings
Machine-learning-based methods outperformed the heuristic approach in most datasets.
The heuristic method showed strong performance on a specific dataset with high conditional unbiasedness.
The multi-modal approach effectively combines methods for better conditional discretization.
Abstract
Performance discretization maps numerical performance values to ordinal categories or performance ranking labels. Norm-referenced performance discretization is extensively applied in human performance evaluation such as grading academic achievements and determining salary increases for employees. These tasks stipulate a common condition that certain performance ranking labels might have no associated performance values and are referred to as conditional discretization. Currently, the only statistical method available for norm-referenced performance discretization is Z score, which merely addresses partial conditions. To achieve a fully conditionally norm-referenced performance discretization, this article proposes four novel approaches enlisting a multi-modal technique that incorporates unsupervised machine-learning algorithms and a heuristic method as well as a novel decision function…
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Taxonomy
TopicsMulti-Criteria Decision Making · Imbalanced Data Classification Techniques · Data Mining Algorithms and Applications