dlordinal: a Python package for deep ordinal classification

Francisco Bérchez-Moreno, Víctor M. Vargas, Rafael Ayllón-Gavilán, David Guijo-Rubio, César Hervás-Martínez +2 more
7/24/2024
cs.LG

Official code: ayrna/dlordinal (73 stars, BSD-3-Clause, PyTorch, last commit 2026-04-23).

Abstract

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.

View on arXivView PDF

Code Implementations(1)

ayrna/dlordinalOfficial100%
PyTorchBSD-3-Clause

Open-source Python toolkit focused on deep learning with ordinal methodologies

734Shell, PythonOct 19, 20236 months agoBSD-3-Clause
deep-learningordinal-classificationpythonpytorchscikit-learn

Similar Papers

Cite this paper

@article{brchezmoreno2024dlordinal,
  title  = {dlordinal: a Python package for deep ordinal classification},
  author = {Francisco Bérchez-Moreno and Víctor M. Vargas and Rafael Ayllón-Gavilán and David Guijo-Rubio and César Hervás-Martínez and Juan C. Fernández and Pedro A. Gutiérrez},
  year   = {2024},
  eprint = {2407.17163},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2407.17163v3}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2407.17163.svg)](https://codeofpaper.com/paper/2407.17163)

Discussion