5 papers
ADABORD: a novel AdaBoost approach for ordinal classification
Rafael Ayllón-Gavilán, Francisco José MartÃnez-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…
From Kellgren-Lawrence to Calcium Pyrophosphate Crystal Deposition: A Soft-Labelling Framework for Knee Osteoarthritis Assessmen
Francisco Bérchez-Moreno, Riccardo Rosati, Maria Chiara Fiorentino +6
Background and objective. Conventional Deep Learning (DL) approaches for Knee Osteoarthritis (KOA) grading rely on one-hot labels, which fail to capture both the ordinal uncertaint…
Splitting criteria for ordinal decision trees: an experimental study
Rafael Ayllón-Gavilán, Francisco José MartÃnez-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, VÃctor 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…
Convolutional and Deep Learning based techniques for Time Series Ordinal Classification
Rafael Ayllón-Gavilán, David Guijo-Rubio, Pedro Antonio Gutiérrez +2
Time Series Classification (TSC) covers the supervised learning problem where input data is provided in the form of series of values observed through repeated measurements over tim…