Taba Binary, Multinomial, and Ordinal Regression Models: New Machine Learning Methods for Classification Article Swipe
Mohammad Tabatabai
,
Derek Wilus
,
Chau-Kuang Chen
,
Karan P. Singh
,
Tim Wallace
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/bioengineering12010002
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/bioengineering12010002
The classification methods of machine learning have been widely used in almost every discipline. A new classification method, called Taba regression, was introduced for analyzing binary, multinomial, and ordinal outcomes. To evaluate the performance of Taba regression, liver cirrhosis data obtained from a Mayo Clinic study were analyzed. The results were then compared with an artificial neural network (ANN), random forest (RF), logistic regression (LR), and probit analysis (PA). The results using cirrhosis data revealed that the Taba regression model could be a competitor to other classification models based on the true positive rate, F-score, accuracy, and area under the receiver operating characteristic curve (AUC). Taba regression can be used by researchers and practitioners as an alternative method of classification in machine learning. In conclusion, the Taba regression provided a reliable result with respect to accuracy, recall, F-score, and AUC when applied to the cirrhosis data.
Related Topics
Concepts
Multinomial logistic regression
Binary classification
Artificial intelligence
Logistic regression
Receiver operating characteristic
Ordinal regression
Random forest
Machine learning
Statistics
Artificial neural network
Regression analysis
Computer science
Regression
Probit model
Mathematics
Support vector machine
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/bioengineering12010002
- https://www.mdpi.com/2306-5354/12/1/2/pdf?version=1735037500
- OA Status
- gold
- References
- 24
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4405736568
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4405736568Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/bioengineering12010002Digital Object Identifier
- Title
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Taba Binary, Multinomial, and Ordinal Regression Models: New Machine Learning Methods for ClassificationWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-12-24Full publication date if available
- Authors
-
Mohammad Tabatabai, Derek Wilus, Chau-Kuang Chen, Karan P. Singh, Tim WallaceList of authors in order
- Landing page
-
https://doi.org/10.3390/bioengineering12010002Publisher landing page
- PDF URL
-
https://www.mdpi.com/2306-5354/12/1/2/pdf?version=1735037500Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
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https://www.mdpi.com/2306-5354/12/1/2/pdf?version=1735037500Direct OA link when available
- Concepts
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Multinomial logistic regression, Binary classification, Artificial intelligence, Logistic regression, Receiver operating characteristic, Ordinal regression, Random forest, Machine learning, Statistics, Artificial neural network, Regression analysis, Computer science, Regression, Probit model, Mathematics, Support vector machineTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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24Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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