DBCore: decision boundaries for coreset selection

Diqi Chen

doi:10.1016/j.patcog.2026.114374

DBCore: decision boundaries for coreset selection

2026-07-01

0

PRE

AI

DOI：10.1016/j.patcog.2026.114374

Original

Original request for help

Save

Abstract

En 中文

Coreset selection for classification often relies on assessing individual sample difficulty or importance, leading to sample-wise or range-based selection, but this can overlook the collective impact on model decision boundaries. Realizing that the representative power a coreset possesses is tightly associated with the decision boundaries a model can form on it, we propose a novel approach that directly optimizes the Decision Boundary (DB) formed by the selected coreset. Specifically, we ask: How can we collectively select samples to create a DB that is globally smoothed yet locally detailed, ensuring maximum generalizability and noise-resilience to the original dataset? To address this, we define two key objectives: (1) Global shape retention-The selected coreset should form a smoothed version of the original DB, preserving its overall structure and preventing overfitting; (2) Local detail preservation-While smoothing prevents overfitting, excessive smoothing risks losing critical nuances. Thus, the selection must also retain key points near the original DB to capture local complexities. We formulate these objectives as a convex quadratic optimization problem with linear constraints and solve it efficiently. Extensive evaluations demonstrate the consistent and substantial advantages of our method over the state-of-the-art coreset selection strategies. The source code is available at https://github.com/diqichen91/DBCore.git.

Keywords:

Coreset selection

Decision boundary reconstruction

Quadratic programming

DBCore: decision boundaries for coreset selection

Abstract

Journal

Organization

Cited Papers