Learning Ordinal–Hierarchical constraints for deep learning classifiers
R. Rosati , L. Romeo, V. Vargas, P. Gutiérrez, E. Frontoni, C. Hervás-Martínez
IEEE Transactions on Neural Networks and Learning Systems, pp. 1-14, 2024 Indexed in JCR. Impact factor: 10.2, Position: 7/144 (Q1D1) in COMPUTER SCIENCE, THEORY & METHODSAbstract
Real-world classification problems may disclose different hierarchical levels where the categories are displayed in an ordinal structure. However, no specific deep learning (DL) models simultaneously learn hierarchical and ordinal constraints while improving generalization performance. To fill this gap, we propose the introduction of two novel ordinal–hierarchical DL methodologies, namely, the hierarchical cumulative link model (HCLM) and hierarchical–ordinal binary decomposition (HOBD), which are able to model the ordinal structure within different hierarchical levels of the labels. In particular, we decompose the hierarchical–ordinal problem into local and global graph paths that may encode an ordinal constraint for each hierarchical level. Thus, we frame this problem as simultaneously minimizing global and local losses. Furthermore, the ordinal constraints are set by two approaches ordinal binary decomposition (OBD) and cumulative link model (CLM) within each global and local function. The effectiveness of the proposed approach is measured on four real-use case datasets concerning industrial, biomedical, computer vision, and financial domains. The extracted results demonstrate a statistically significant improvement to state-of-the-art nominal, ordinal, and hierarchical approaches.
Cite this publication
BibTex
@article{rosati2024learning,
author = {Riccardo Rosati and Luca Romeo and Víctor Manuel Vargas and Pedro Antonio Gutiérrez and Emanuele Frontoni and César Hervás-Martínez},
title = {Learning Ordinal–Hierarchical constraints for deep learning classifiers},
journal = {IEEE Transactions on Neural Networks and Learning Systems},
year = {2024},
volume = {0},
number = {0},
pages = {1--14},
doi = {10.1109/TNNLS.2024.3360641}
}
APA
Rosati, R., Romeo, L., Vargas, V., Gutiérrez, P., Frontoni, E., Hervás-Martínez, C. (2024). Learning Ordinal–Hierarchical constraints for deep learning classifiers. IEEE Transactions on Neural Networks and Learning Systems, 0(0), 1-14.
CV
R. Rosati (CA), L. Romeo, V.M. Vargas, P.A. Gutiérrez, E. Frontoni, C. Hervás-Martínez, (3/6) "Learning Ordinal–Hierarchical constraints for deep learning classifiers", IEEE Transactions on Neural Networks and Learning Systems, Vol. 0(0), pp. 1-14, 2024. (Q1, D1, IF: 10.2)
RIS
TY - JOUR T1 - Learning Ordinal–Hierarchical constraints for deep learning classifiers AU - Rosati, Riccardo AU - Romeo, Luca AU - Vargas, Víctor Manuel AU - Gutiérrez, Pedro Antonio AU - Frontoni, Emanuele AU - Hervás-Martínez, César JO - IEEE Transactions on Neural Networks and Learning Systems VL - 0 IS - 0 SP - 1 EP - 14 PY - 2024 DO - 10.1109/TNNLS.2024.3360641 ER -