.png){kind=icon}
.png){kind=icon}
Return
On-Demand Augmentation for Long-Tail Data Through Subset- and Topic-Driven Sparsity Identification Framework
.png){kind=icon}
OA
AI
DOI：10.1007/s41019-025-00334-6.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Large-scale, multi-dimensional mixed datasets are characterized by the pervasive "long-tail distribution." This phenomenon results in data sparsity in subspaces defined by multi-dimensional attribute combinations. This sparsity severely hinders data-driven analysis and insights. Existing data-augmentation methods primarily focus on single dimensions, ignoring the complex, intrinsic multi-dimensional correlations of the real world, which leads to generated samples that lack logic and realism. To address this challenge, we propose a systematic, on-demand, and fine-grained sample augmentation framework. Our core idea is to precisely locate and augment data-sparse regions from a multi-dimensional combinatorial perspective. For textual data, we have designe two flexible augmentation modes. The first, "augmentation by subset scope", adopts a strategy of model merging and incremental updates. The second, "augmentation by topic", proposes a heuristic search algorithm based on the "Explore-Exploit" paradigm. For numerical data, we pre-construct a global distribution index to achieve efficient identification of sparse intervals. In the sample generation phase, we combine Large Language Models, Retrieval-Augmented Generation, and Chain-of-Thought techniques to ensure that the generated samples meet high-fidelity standards in semantic logic and contextual style. Extensive experiments on real-world datasets demonstrate that our method outperforms baseline approaches in query response speed and the efficiency of sparse region discovery, while maintaining topic coherence and accuracy.
Keywords:
Long-tail distribution
Topic modeling
Sparsity identification
Sample augmentation
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
D
IF:
4.6
Papers:
248
Citations:
665