ADABORD: a novel AdaBoost approach for ordinal classification

• URL: http://arxiv.org/abs/2607.21003v1
• Date: Thu, 23 Jul 2026 07:37:57 GMT
• Title: ADABORD: a novel AdaBoost approach for ordinal classification
• Abstract summary: Ordinal Classification (OC) deals with classification tasks where the classes follow a natural order.<n>This work introduces ADABORD, an AdaBoost framework specifically designed for ordinal classification problems.
• Score: 3.896552652033012
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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 the ordinal information, treating the problem as nominal classification and thereby losing performance potential. In this work, ADABORD, an AdaBoost framework specifically designed for ordinal classification problems, is introduced. The ordinal nature of the classes is incorporated into two key components of the well-known AdaBoost algorithm: 1) the base estimator, where decision trees with the ordinal Gini splitting criterion are proposed; 2) the error function used to update sample weights at each stage and the weights of the classifier in the final ensemble model, given by the absolute ranked probability score, a measure that accounts for both the ordering and the distance between classes. ADABORD is extensively compared against seven state-of-the-art methods on the TOC-UCO repository, the largest benchmark collection for OC to date. The experimental results, supported by statistical analysis, show that ADABORD significantly outperforms competing methods, particularly on datasets with five or more classes, where the ordinal structure becomes more pronounced. Source code, along with all experimental protocols, is publicly available to ensure reproducibility and facilitate future research in OC.

Related papers

• Generalized Category Discovery via Reciprocal Learning and Class-Wise Distribution Regularization [6.696520328216944]
  Generalized Category Discovery (GCD) aims to identify unlabeled samples by leveraging the base knowledge from labeled ones.<n>Recent parametric-based methods suffer from inferior base discrimination due to unreliable self-supervision.<n>We propose a Reciprocal Learning Framework (RLF) that introduces an auxiliary branch devoted to base classification.
  arXiv  Detail & Related papers  (2025-06-03T00:12:39Z)
• Splitting criteria for ordinal decision trees: an experimental study [6.575723870852787]
  Ordinal Classification (OC) addresses those classification tasks where the labels exhibit a natural order.<n>OC takes the ordinal relationship into account, producing more accurate and relevant results.<n>This work provides a comprehensive survey of ordinal splitting criteria, standardising the notations used in the literature to enhance clarity and consistency.
  arXiv  Detail & Related papers  (2024-12-18T10:41:44Z)
• Improving the classification of extreme classes by means of loss regularisation and generalised beta distributions [8.640930010669042]
  We propose a unimodal regularisation approach to improve the classification performance of the first and last classes. Performance in the extreme classes is compared using a new metric that takes into account their sensitivities. The results for the proposed metric show that the generalised beta distribution generally improves classification performance in the extreme classes.
  arXiv  Detail & Related papers  (2024-07-17T08:57:42Z)
• Regularization-Based Methods for Ordinal Quantification [49.606912965922504]
  We study the ordinal case, i.e., the case in which a total order is defined on the set of n>2 classes. We propose a novel class of regularized OQ algorithms, which outperforms existing algorithms in our experiments.
  arXiv  Detail & Related papers  (2023-10-13T16:04:06Z)
• Bipartite Ranking Fairness through a Model Agnostic Ordering Adjustment [54.179859639868646]
  We propose a model agnostic post-processing framework xOrder for achieving fairness in bipartite ranking. xOrder is compatible with various classification models and ranking fairness metrics, including supervised and unsupervised fairness metrics. We evaluate our proposed algorithm on four benchmark data sets and two real-world patient electronic health record repositories.
  arXiv  Detail & Related papers  (2023-07-27T07:42:44Z)
• Anomaly Detection using Ensemble Classification and Evidence Theory [62.997667081978825]
  We present a novel approach for novel detection using ensemble classification and evidence theory. A pool selection strategy is presented to build a solid ensemble classifier. We use uncertainty for the anomaly detection approach.
  arXiv  Detail & Related papers  (2022-12-23T00:50:41Z)
• Parametric Classification for Generalized Category Discovery: A Baseline Study [70.73212959385387]
  Generalized Category Discovery (GCD) aims to discover novel categories in unlabelled datasets using knowledge learned from labelled samples. We investigate the failure of parametric classifiers, verify the effectiveness of previous design choices when high-quality supervision is available, and identify unreliable pseudo-labels as a key problem. We propose a simple yet effective parametric classification method that benefits from entropy regularisation, achieves state-of-the-art performance on multiple GCD benchmarks and shows strong robustness to unknown class numbers.
  arXiv  Detail & Related papers  (2022-11-21T18:47:11Z)
• Ranking hierarchical multi-label classification results with mLPRs [4.869182515096001]
  We focus on the less attended second-stage question while adhering to the given class hierarchy.<n>We introduce a new objective function, called CATCH, to ensure reasonable classification performance.<n>Our approach was evaluated on a synthetic dataset and two real datasets.
  arXiv  Detail & Related papers  (2022-05-16T17:43:35Z)
• Binary Classification from Multiple Unlabeled Datasets via Surrogate Set Classification [94.55805516167369]
  We propose a new approach for binary classification from m U-sets for $mge2$. Our key idea is to consider an auxiliary classification task called surrogate set classification (SSC)
  arXiv  Detail & Related papers  (2021-02-01T07:36:38Z)
• Convolutional Ordinal Regression Forest for Image Ordinal Estimation [52.67784321853814]
  We propose a novel ordinal regression approach, termed Convolutional Ordinal Regression Forest or CORF, for image ordinal estimation. The proposed CORF integrates ordinal regression and differentiable decision trees with a convolutional neural network for obtaining precise and stable global ordinal relationships. The effectiveness of the proposed CORF is verified on two image ordinal estimation tasks, showing significant improvements and better stability over the state-of-the-art ordinal regression methods.
  arXiv  Detail & Related papers  (2020-08-07T10:41:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.