Unbiased machine learning-assisted approach for conditional discretization of human performances
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. Th...
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2025-04-01
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| Series: | PeerJ Computer Science |
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| Online Access: | https://peerj.com/articles/cs-2804.pdf |
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| author | Thepparit Banditwattanawong Masawee Masdisornchote |
| author_facet | Thepparit Banditwattanawong Masawee Masdisornchote |
| author_sort | Thepparit Banditwattanawong |
| collection | DOAJ |
| description | 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 ensuring conditional unbiasedness. The machine-learning-based methods demonstrate superiority over the heuristic one across most testing data sets, achieving a conditional unbiasedness degree ranging from 0.11 to 0.82. On the other hand, the heuristic method notably outperforms for a specific data set, exhibiting a conditional unbiasedness degree up to 0.76. Leveraging the strengths of these constituent methods enable the effectiveness of the proposed multi-modal approach for conditionally norm-referenced performance discretization. |
| format | Article |
| id | doaj-art-5f716147d4574c1c8fb34cb0f205b5c7 |
| institution | DOAJ |
| issn | 2376-5992 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj-art-5f716147d4574c1c8fb34cb0f205b5c72025-08-20T03:13:22ZengPeerJ Inc.PeerJ Computer Science2376-59922025-04-0111e280410.7717/peerj-cs.2804Unbiased machine learning-assisted approach for conditional discretization of human performancesThepparit Banditwattanawong0Masawee Masdisornchote1Department of Computer Science, Faculty of Science, Kasetsart University, Krung Thep Maha Nakhon, ThailandSchool of Information Technology, Sripatum University, Krung Thep Maha Nakhon, ThailandPerformance 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 ensuring conditional unbiasedness. The machine-learning-based methods demonstrate superiority over the heuristic one across most testing data sets, achieving a conditional unbiasedness degree ranging from 0.11 to 0.82. On the other hand, the heuristic method notably outperforms for a specific data set, exhibiting a conditional unbiasedness degree up to 0.76. Leveraging the strengths of these constituent methods enable the effectiveness of the proposed multi-modal approach for conditionally norm-referenced performance discretization.https://peerj.com/articles/cs-2804.pdfData analysisHuman performanceConditional performance discretizationNorm referenced evaluationUnbiasednessMulti-modal technique |
| spellingShingle | Thepparit Banditwattanawong Masawee Masdisornchote Unbiased machine learning-assisted approach for conditional discretization of human performances PeerJ Computer Science Data analysis Human performance Conditional performance discretization Norm referenced evaluation Unbiasedness Multi-modal technique |
| title | Unbiased machine learning-assisted approach for conditional discretization of human performances |
| title_full | Unbiased machine learning-assisted approach for conditional discretization of human performances |
| title_fullStr | Unbiased machine learning-assisted approach for conditional discretization of human performances |
| title_full_unstemmed | Unbiased machine learning-assisted approach for conditional discretization of human performances |
| title_short | Unbiased machine learning-assisted approach for conditional discretization of human performances |
| title_sort | unbiased machine learning assisted approach for conditional discretization of human performances |
| topic | Data analysis Human performance Conditional performance discretization Norm referenced evaluation Unbiasedness Multi-modal technique |
| url | https://peerj.com/articles/cs-2804.pdf |
| work_keys_str_mv | AT thepparitbanditwattanawong unbiasedmachinelearningassistedapproachforconditionaldiscretizationofhumanperformances AT masaweemasdisornchote unbiasedmachinelearningassistedapproachforconditionaldiscretizationofhumanperformances |