9 citations · 9 across the 3 of their papers we have counts for
6 papers
ADABORD: a novel AdaBoost approach for ordinal classification
Rafael Ayllón-Gavilán, Francisco José Martínez-Estudillo, David Guijo-Rubio +2
Ordinal Classification (OC) deals with classification tasks where the classes follow a natural order. Despite the progress in OC, many existing approaches fail to fully leverage th…
A novel ordinal multi-view aggregation scheme for oak defoliation
Francisco Bérchez-Moreno, Ricardo Enrique Hernández-Lambraño, David Guijo-Rubio +4
Forest decline driven by climate and biotic stressors threatens ecosystem functioning, making accurate monitoring of tree health essential. In this work, we address tree defoliatio…
TOC-UCO: a comprehensive repository of tabular ordinal classification datasets
Rafael Ayllón-Gavilán, David Guijo-Rubio, Antonio Manuel Gómez-Orellana +3
An ordinal classification (OC) problem corresponds to a special type of classification characterised by the presence of a natural order relationship among the classes. This type of…
Splitting criteria for ordinal decision trees: an experimental study
Rafael Ayllón-Gavilán, Francisco José Martínez-Estudillo, David Guijo-Rubio +2
Ordinal Classification (OC) addresses those classification tasks where the labels exhibit a natural order. Unlike nominal classification, which treats all classes as mutually exclu…
dlordinal: a Python package for deep ordinal classification
Francisco Bérchez-Moreno, Víctor M. Vargas, Rafael Ayllón-Gavilán +4
dlordinal is a new Python library that unifies many recent deep ordinal classification methodologies available in the literature. Developed using PyTorch as underlying framework, i…
aeon: a Python toolkit for learning from time series
Matthew Middlehurst, Ali Ismail-Fawaz, Antoine Guillaume +8
aeon is a unified Python 3 library for all machine learning tasks involving time series. The package contains modules for time series forecasting, classification, extrinsic regress…