Adaptive kernel-density approach for imbalanced binary classification

• URL: http://arxiv.org/abs/2510.04046v1
• Date: Sun, 05 Oct 2025 05:45:00 GMT
• Title: Adaptive kernel-density approach for imbalanced binary classification
• Authors: Kotaro J. Nishimura, Yuichi Sakumura, Kazushi Ikeda,
• Abstract summary: Class imbalance is a common challenge in real-world binary classification tasks.<n>We propose a novel approach called Kernel-density-Oriented Threshold Adjustment with Regional Optimization (KOTARO)<n>In KOTARO, the bandwidth of Gaussian basis functions is dynamically tuned based on the estimated density around each sample.
• Score: 2.3126620160776845
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Class imbalance is a common challenge in real-world binary classification tasks, often leading to predictions biased toward the majority class and reduced recognition of the minority class. This issue is particularly critical in domains such as medical diagnosis and anomaly detection, where correct classification of minority classes is essential. Conventional methods often fail to deliver satisfactory performance when the imbalance ratio is extremely severe. To address this challenge, we propose a novel approach called Kernel-density-Oriented Threshold Adjustment with Regional Optimization (KOTARO), which extends the framework of kernel density estimation (KDE) by adaptively adjusting decision boundaries according to local sample density. In KOTARO, the bandwidth of Gaussian basis functions is dynamically tuned based on the estimated density around each sample, thereby enhancing the classifier's ability to capture minority regions. We validated the effectiveness of KOTARO through experiments on both synthetic and real-world imbalanced datasets. The results demonstrated that KOTARO outperformed conventional methods, particularly under conditions of severe imbalance, highlighting its potential as a promising solution for a wide range of imbalanced classification problems

Related papers

• Rebalancing with Calibrated Sub-classes (RCS): A Statistical Fusion-based Framework for Robust Imbalanced Classification across Modalities [16.993547305381327]
  Rebalancing with Calibrated Sub-classes (RCS) is a novel distribution calibration framework for robust imbalanced classification.<n>RCS fuses statistical information from the majority and intermediate class distributions via a weighted mixture of Gaussian components.
  arXiv  Detail & Related papers  (2025-10-10T00:06:13Z)
• Learning Majority-to-Minority Transformations with MMD and Triplet Loss for Imbalanced Classification [0.5390869741300152]
  Class imbalance in supervised classification often degrades model performance by biasing predictions toward the majority class.<n>We introduce an oversampling framework that learns a parametric transformation to map majority samples into the minority distribution.<n>Our approach minimizes the mean maximum discrepancy (MMD) between transformed and true minority samples for global alignment.
  arXiv  Detail & Related papers  (2025-09-15T01:47:29Z)
• Beyond Rebalancing: Benchmarking Binary Classifiers Under Class Imbalance Without Rebalancing Techniques [3.7660066212240744]
  Class imbalance poses a significant challenge to supervised classification, particularly in critical domains like medical diagnostics and anomaly detection.<n>We systematically evaluate the robustness of a diverse set of binary classifiers across both real-world and synthetic datasets.<n>Results confirm that classification becomes more difficult as data complexity increases and the minority class size decreases.
  arXiv  Detail & Related papers  (2025-09-09T11:28:34Z)
• Noise-Adaptive Conformal Classification with Marginal Coverage [53.74125453366155]
  We introduce an adaptive conformal inference method capable of efficiently handling deviations from exchangeability caused by random label noise.<n>We validate our method through extensive numerical experiments demonstrating its effectiveness on synthetic and real data sets.
  arXiv  Detail & Related papers  (2025-01-29T23:55:23Z)
• An Adaptive Cost-Sensitive Learning and Recursive Denoising Framework for Imbalanced SVM Classification [12.986535715303331]
  Category imbalance is one of the most popular and important issues in the domain of classification.<n>We propose a robust learning algorithm based on adaptive cost-sensitivity and recursion.<n> Experimental results show that the proposed general framework is superior to traditional methods in Accuracy, G-mean, Recall and F1-score.
  arXiv  Detail & Related papers  (2024-03-13T09:43:14Z)
• Uncertainty-guided Boundary Learning for Imbalanced Social Event Detection [64.4350027428928]
  We propose a novel uncertainty-guided class imbalance learning framework for imbalanced social event detection tasks. Our model significantly improves social event representation and classification tasks in almost all classes, especially those uncertain ones.
  arXiv  Detail & Related papers  (2023-10-30T03:32:04Z)
• Deep Imbalanced Regression via Hierarchical Classification Adjustment [50.19438850112964]
  Regression tasks in computer vision are often formulated into classification by quantizing the target space into classes. The majority of training samples lie in a head range of target values, while a minority of samples span a usually larger tail range. We propose to construct hierarchical classifiers for solving imbalanced regression tasks. Our novel hierarchical classification adjustment (HCA) for imbalanced regression shows superior results on three diverse tasks.
  arXiv  Detail & Related papers  (2023-10-26T04:54:39Z)
• Class-Imbalanced Graph Learning without Class Rebalancing [62.1368829847041]
  Class imbalance is prevalent in real-world node classification tasks and poses great challenges for graph learning models. In this work, we approach the root cause of class-imbalance bias from an topological paradigm. We devise a lightweight topological augmentation framework BAT to mitigate the class-imbalance bias without class rebalancing.
  arXiv  Detail & Related papers  (2023-08-27T19:01:29Z)
• Proposal Distribution Calibration for Few-Shot Object Detection [65.19808035019031]
  In few-shot object detection (FSOD), the two-step training paradigm is widely adopted to mitigate the severe sample imbalance. Unfortunately, the extreme data scarcity aggravates the proposal distribution bias, hindering the RoI head from evolving toward novel classes. We introduce a simple yet effective proposal distribution calibration (PDC) approach to neatly enhance the localization and classification abilities of the RoI head.
  arXiv  Detail & Related papers  (2022-12-15T05:09:11Z)
• Compound Batch Normalization for Long-tailed Image Classification [77.42829178064807]
  We propose a compound batch normalization method based on a Gaussian mixture. It can model the feature space more comprehensively and reduce the dominance of head classes. The proposed method outperforms existing methods on long-tailed image classification.
  arXiv  Detail & Related papers  (2022-12-02T07:31:39Z)
• Label-Imbalanced and Group-Sensitive Classification under Overparameterization [32.923780772605596]
  Label-imbalanced and group-sensitive classification seeks to appropriately modify standard training algorithms to optimize relevant metrics. We show that a logit-adjusted loss modification to standard empirical risk minimization might be ineffective in general. We show that our results extend naturally to binary classification with sensitive groups, thus treating the two common types of imbalances (label/group) in a unifying way.
  arXiv  Detail & Related papers  (2021-03-02T08:09:43Z)
• Provable tradeoffs in adversarially robust classification [96.48180210364893]
  We develop and leverage new tools, including recent breakthroughs from probability theory on robust isoperimetry. Our results reveal fundamental tradeoffs between standard and robust accuracy that grow when data is imbalanced.
  arXiv  Detail & Related papers  (2020-06-09T09:58:19Z)

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.