dlordinal: a Python package for deep ordinal classification
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866929669264113664 |
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| author | Bérchez-Moreno, Francisco Vargas, Víctor M. Ayllón-Gavilán, Rafael Guijo-Rubio, David Hervás-Martínez, César Fernández, Juan C. Gutiérrez, Pedro A. |
| author_facet | Bérchez-Moreno, Francisco Vargas, Víctor M. Ayllón-Gavilán, Rafael Guijo-Rubio, David Hervás-Martínez, César Fernández, Juan C. Gutiérrez, Pedro A. |
| contents | dlordinal is a new Python library that unifies many recent deep ordinal classification methodologies available in the literature. Developed using PyTorch as underlying framework, it implements the top performing state-of-the-art deep learning techniques for ordinal classification problems. Ordinal approaches are designed to leverage the ordering information present in the target variable. Specifically, it includes loss functions, various output layers, dropout techniques, soft labelling methodologies, and other classification strategies, all of which are appropriately designed to incorporate the ordinal information. Furthermore, as the performance metrics to assess novel proposals in ordinal classification depend on the distance between target and predicted classes in the ordinal scale, suitable ordinal evaluation metrics are also included. dlordinal is distributed under the BSD-3-Clause license and is available at https://github.com/ayrna/dlordinal. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_17163 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | dlordinal: a Python package for deep ordinal classification Bérchez-Moreno, Francisco Vargas, Víctor M. Ayllón-Gavilán, Rafael Guijo-Rubio, David Hervás-Martínez, César Fernández, Juan C. Gutiérrez, Pedro A. Machine Learning dlordinal is a new Python library that unifies many recent deep ordinal classification methodologies available in the literature. Developed using PyTorch as underlying framework, it implements the top performing state-of-the-art deep learning techniques for ordinal classification problems. Ordinal approaches are designed to leverage the ordering information present in the target variable. Specifically, it includes loss functions, various output layers, dropout techniques, soft labelling methodologies, and other classification strategies, all of which are appropriately designed to incorporate the ordinal information. Furthermore, as the performance metrics to assess novel proposals in ordinal classification depend on the distance between target and predicted classes in the ordinal scale, suitable ordinal evaluation metrics are also included. dlordinal is distributed under the BSD-3-Clause license and is available at https://github.com/ayrna/dlordinal. |
| title | dlordinal: a Python package for deep ordinal classification |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2407.17163 |