skip to main content
10.1145/3297280.3297560acmconferencesArticle/Chapter ViewAbstractPublication PagessacConference Proceedingsconference-collections
poster

An iterative oversampling approach for ordinal classification

Published: 08 April 2019 Publication History

Abstract

The machine learning field has grown considerably in the last years. There are, however, some problems still to be solved. The characteristics of the training sets, for instance, are known to affect the classifiers performance. Here, and inspired by medical applications, we are interested in studying datasets that are both ordinal and imbalanced. Ordinal datasets present labels where only the relative ordering between different values is significant. Imbalanced datasets have very different quantity of examples per class.
Building upon our previous work, we make three new contributions, (1) extend the number of classifiers, (2) evaluate two techniques to balance intermediate train sets in binary decomposition methods (often used in multi-class contexts and ordinal ones in particular), and (3) propose a new, iterative, classifier-based over-sampling algorithm that we name InCuBAtE. Experiments were made on 6 private datasets, concerning the assessment of response to treatment on oncologic diseases, and 15 public datasets widely used in the literature. When compared with our previous work, results have improved (or remained the same) for 4 of the 6 private datasets and for 11 out of the 15 public datasets.

References

[1]
S Bessa, I Domingues, J S Cardosos, P Passarinho, P Cardoso, V Rodrigues, and F Lage. 2014. Normal breast identification in screening mammography: a study on 18 000 images. In IEEE International Conference on Bioinformatics and Biomedicine. 325--330.
[2]
R Cruz, K Fernandes, J F P Costa, and J S Cardoso. 2017. Constraining Type II Error: Building Intentionally Biased Classifiers. In International Work-Conference on Artificial Neural Networks. Springer, 549--560.
[3]
R Cruz, K Fernandes, J F P Costa, M P Ortiz, and J S Cardoso. 2018. Binary ranking for ordinal class imbalance. Pattern Analysis and Applications (2018), 1--9.
[4]
I Domingues, J P Amorim, P H Abreu, H Duarte, and J Santos. 2018. Evaluation of oversampling data balancing techniques in the context of ordinal classification. In IEEE International Joint Conference on Neural Networks. 5691--5698.
[5]
E Frank and M Hall. 2001. A Simple Approach to Ordinal Classification. In European Conference on Machine Learning. Springer, 145--156.
[6]
P A Gutiérrez, M Pérez-Ortiz, J Sánchez-Monedero, F Fernández-Navarro, and C Hervás-Martínez. 2015. Ordinal regression methods: survey and experimental study. IEEE Transactions on Knowledge and Data Engineering 28, 1 (2015), 127--146.
[7]
T Hastie, R Tibshirani, and J H Friedman. 2009. The Elements of Statistical Learning. Springer. 745 pages.
[8]
M Pérez-Ortiz, M Cruz-Ramírez, M D Ayllón-Terán, N Heaton, R Ciria, and C Hervás-Martínez. 2014. An organ allocation system for liver transplantation based on ordinal regression. Applied Soft Computing Journal 14 (2014), 88--98.
[9]
M S Santos, P H Abreu, P J García-Laencina, A Simão, and A Carvalho. 2015. A new cluster-based oversampling method for improving survival prediction of hepatocellular carcinoma patients. Journal of Biomedical Informatics 58 (2015), 49--59.

Cited By

View all
  • (2025)Predicting Grades in the “Introduction to Clinical Databases” Course: Dataset Creation, Pipeline Design, and Model EvaluationArtificial Intelligence in Education Technologies: New Development and Innovative Practices10.1007/978-981-97-9255-9_17(255-267)Online publication date: 1-Jan-2025
  • (2024)Mammogram Retrieval System: Aggregating Image Classifiers for Enhanced Breast Cancer DiagnosisProceedings of the 2024 6th International Conference on Intelligent Medicine and Image Processing10.1145/3669828.3669829(1-8)Online publication date: 26-Apr-2024
  • (2024)Distinguishing between Crohn’s disease and ulcerative colitis using deep learning models with interpretabilityPattern Analysis & Applications10.1007/s10044-023-01206-327:1Online publication date: 25-Jan-2024
  • Show More Cited By

Index Terms

  1. An iterative oversampling approach for ordinal classification

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Conferences
    SAC '19: Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing
    April 2019
    2682 pages
    ISBN:9781450359337
    DOI:10.1145/3297280
    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

    Sponsors

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 08 April 2019

    Check for updates

    Author Tags

    1. imbalanced datasets
    2. ordinal classification
    3. oversampling

    Qualifiers

    • Poster

    Funding Sources

    Conference

    SAC '19
    Sponsor:

    Acceptance Rates

    Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

    Upcoming Conference

    SAC '25
    The 40th ACM/SIGAPP Symposium on Applied Computing
    March 31 - April 4, 2025
    Catania , Italy

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • Downloads (Last 12 months)7
    • Downloads (Last 6 weeks)1
    Reflects downloads up to 19 Feb 2025

    Other Metrics

    Citations

    Cited By

    View all
    • (2025)Predicting Grades in the “Introduction to Clinical Databases” Course: Dataset Creation, Pipeline Design, and Model EvaluationArtificial Intelligence in Education Technologies: New Development and Innovative Practices10.1007/978-981-97-9255-9_17(255-267)Online publication date: 1-Jan-2025
    • (2024)Mammogram Retrieval System: Aggregating Image Classifiers for Enhanced Breast Cancer DiagnosisProceedings of the 2024 6th International Conference on Intelligent Medicine and Image Processing10.1145/3669828.3669829(1-8)Online publication date: 26-Apr-2024
    • (2024)Distinguishing between Crohn’s disease and ulcerative colitis using deep learning models with interpretabilityPattern Analysis & Applications10.1007/s10044-023-01206-327:1Online publication date: 25-Jan-2024
    • (2023)Knowledge Distillation of Vision Transformers and Convolutional Networks to Predict Inflammatory Bowel DiseaseProgress in Pattern Recognition, Image Analysis, Computer Vision, and Applications10.1007/978-3-031-49018-7_27(374-390)Online publication date: 27-Nov-2023
    • (2021)An Iterative Algorithm for Semisupervised Classification of Hotspots on Bone Scintigraphies of Patients with Prostate CancerJournal of Imaging10.3390/jimaging70801487:8(148)Online publication date: 17-Aug-2021
    • (2021)Scalable Kernel Ordinal Regression via Doubly Stochastic GradientsIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2020.301593732:8(3677-3689)Online publication date: Aug-2021
    • (2020)Classification of oesophagic early-stage cancers: deep learning versus traditional learning approaches2020 IEEE 20th International Conference on Bioinformatics and Bioengineering (BIBE)10.1109/BIBE50027.2020.00127(746-751)Online publication date: Oct-2020
    • (2019)Automatic Generation of Lymphoma Post-Treatment PETs using Conditional-GANs2019 Digital Image Computing: Techniques and Applications (DICTA)10.1109/DICTA47822.2019.8945835(1-6)Online publication date: Dec-2019

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Figures

    Tables

    Media

    Share

    Share

    Share this Publication link

    Share on social media