Buffered AUC maximization for scoring systems via mixed-integer optimization

• URL: http://arxiv.org/abs/2601.05544v2
• Date: Tue, 13 Jan 2026 01:58:15 GMT
• Title: Buffered AUC maximization for scoring systems via mixed-integer optimization
• Authors: Moe Shiina, Shunnosuke Ikeda, Yuichi Takano,
• Abstract summary: A scoring system is a linear classifier composed of a small number of explanatory variables, each assigned a small integer coefficient.<n>Several previous studies have used mixed-integer optimization (MIO) techniques to develop scoring systems for binary classification.<n>This research contributes to the advancement of MIO techniques for developing highly interpretable classification models.
• Score: 1.8224668251608893
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A scoring system is a linear classifier composed of a small number of explanatory variables, each assigned a small integer coefficient. This system is highly interpretable and allows predictions to be made with simple manual calculations without the need for a calculator. Several previous studies have used mixed-integer optimization (MIO) techniques to develop scoring systems for binary classification; however, they have not focused on directly maximizing AUC (i.e., area under the receiver operating characteristic curve), even though AUC is recognized as an essential evaluation metric for scoring systems. Our goal herein is to establish an effective MIO framework for constructing scoring systems that directly maximize the buffered AUC (bAUC) as the tightest concave lower bound on AUC. Our optimization model is formulated as a mixed-integer linear optimization (MILO) problem that maximizes bAUC subject to a group sparsity constraint for limiting the number of questions in the scoring system. Computational experiments using publicly available real-world datasets demonstrate that our MILO method can build scoring systems with superior AUC values compared to the baseline methods based on regularization and stepwise regression. This research contributes to the advancement of MIO techniques for developing highly interpretable classification models.

Related papers

• Minimally Supervised Learning using Topological Projections in Self-Organizing Maps [55.31182147885694]
  We introduce a semi-supervised learning approach based on topological projections in self-organizing maps (SOMs) Our proposed method first trains SOMs on unlabeled data and then a minimal number of available labeled data points are assigned to key best matching units (BMU) Our results indicate that the proposed minimally supervised model significantly outperforms traditional regression techniques.
  arXiv  Detail & Related papers  (2024-01-12T22:51:48Z)
• MISS: Multiclass Interpretable Scoring Systems [13.902264070785986]
  We present a machine-learning approach for constructing Multiclass Interpretable Scoring Systems (MISS) MISS is a fully data-driven methodology for single, sparse, and user-friendly scoring systems for multiclass classification problems. Results indicate that our approach is competitive with other machine learning models in terms of classification performance metrics and provides well-calibrated class probabilities.
  arXiv  Detail & Related papers  (2024-01-10T10:57:12Z)
• Balanced Self-Paced Learning for AUC Maximization [88.53174245457268]
  Existing self-paced methods are limited to pointwise AUC. Our algorithm converges to a stationary point on the basis of closed-form solutions.
  arXiv  Detail & Related papers  (2022-07-08T02:09:32Z)
• Feature subset selection for kernel SVM classification via mixed-integer optimization [0.7734726150561088]
  We study the mixed-integer optimization (MIO) approach to feature subset selection in nonlinear kernel support vector machines (SVMs) for binary classification. First proposed for linear regression in the 1970s, this approach has recently moved into the spotlight with advances in optimization algorithms and computer hardware. We propose a mixed-integer linear optimization (MILO) formulation based on the kernel-target alignment for feature subset selection, and this MILO problem can be solved to optimality using optimization software.
  arXiv  Detail & Related papers  (2022-05-28T04:01:40Z)
• Large-scale Optimization of Partial AUC in a Range of False Positive Rates [51.12047280149546]
  The area under the ROC curve (AUC) is one of the most widely used performance measures for classification models in machine learning. We develop an efficient approximated gradient descent method based on recent practical envelope smoothing technique. Our proposed algorithm can also be used to minimize the sum of some ranked range loss, which also lacks efficient solvers.
  arXiv  Detail & Related papers  (2022-03-03T03:46:18Z)
• Pretrained Cost Model for Distributed Constraint Optimization Problems [37.79733538931925]
  Distributed Constraint Optimization Problems (DCOPs) are an important subclass of optimization problems. We propose a novel directed acyclic graph schema representation for DCOPs and leverage the Graph Attention Networks (GATs) to embed graph representations. Our model, GAT-PCM, is then pretrained with optimally labelled data in an offline manner, so as to boost a broad range of DCOP algorithms.
  arXiv  Detail & Related papers  (2021-12-08T09:24:10Z)
• Learning with Multiclass AUC: Theory and Algorithms [141.63211412386283]
  Area under the ROC curve (AUC) is a well-known ranking metric for problems such as imbalanced learning and recommender systems. In this paper, we start an early trial to consider the problem of learning multiclass scoring functions via optimizing multiclass AUC metrics.
  arXiv  Detail & Related papers  (2021-07-28T05:18:10Z)
• Adaptive Local Kernels Formulation of Mutual Information with Application to Active Post-Seismic Building Damage Inference [1.066048003460524]
  Post-earthquake regional damage assessment of buildings is an expensive task. The information theoretic measure of mutual information is one of the most effective criteria to evaluate the effectiveness of the samples. A local kernels strategy was proposed to reduce the computational costs, but the adaptability of the kernels to the observed labels was not considered. In this article, an adaptive local kernels methodology is developed that allows for the conformability of the kernels to the observed output data.
  arXiv  Detail & Related papers  (2021-05-24T18:34:46Z)
• Learning by Minimizing the Sum of Ranked Range [58.24935359348289]
  We introduce the sum of ranked range (SoRR) as a general approach to form learning objectives. A ranked range is a consecutive sequence of sorted values of a set of real numbers. We explore two applications in machine learning of the minimization of the SoRR framework, namely the AoRR aggregate loss for binary classification and the TKML individual loss for multi-label/multi-class classification.
  arXiv  Detail & Related papers  (2020-10-05T01:58:32Z)
• Communication-Efficient Distributed Stochastic AUC Maximization with Deep Neural Networks [50.42141893913188]
  We study a distributed variable for large-scale AUC for a neural network as with a deep neural network. Our model requires a much less number of communication rounds and still a number of communication rounds in theory. Our experiments on several datasets show the effectiveness of our theory and also confirm our theory.
  arXiv  Detail & Related papers  (2020-05-05T18:08:23Z)

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.