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We propose Selective Few-Random-Shot Augmentation (SFRSA), a generate-then-select framework for low-resource imbalanced text classification. SFRSA first over-generates minority-class candidates through few-random-shot LLM prompting, then selects a fixed-budget subset by optimizing the trade-off between relevance to minority examples and diversity among selected samples in embedding space. To operationalize this idea, we develop and compare three selection modules: centroid-guided clustering (CGC), determinantal point process (DPP) selection, and a training-aware influence-utility DPP (IU-DPP) variant. Across three benchmark datasets under training sizes of 500, 1000, and 2000 and imbalance ratios of 4:1 and 9:1, SFRSA consistently improves minority-sensitive performance over classic text augmentation methods and standard LLM-based generation baselines, with the largest gains appearing in the most data-scarce settings. A similarity\u2013diversity analysis links the selection objective to downstream gains, showing that SFRSA preserves minority semantics while avoiding near-duplicate generations. The method is model-agnostic and can be integrated into existing NLP pipelines to improve classification in low-resource, imbalanced settings.<\/jats:p>","DOI":"10.1007\/s10489-026-07299-7","type":"journal-article","created":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T10:09:04Z","timestamp":1782382144000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Improving text classification on small-sized, imbalanced datasets with selective few-random-shot augmentation"],"prefix":"10.1007","volume":"56","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0304-882X","authenticated-orcid":false,"given":"Lingshu","family":"Hu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peter","family":"Dolan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Can","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenbo","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bin","family":"Pang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"Shang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,25]]},"reference":[{"key":"7299_CR1","doi-asserted-by":"publisher","unstructured":"Welbers K, Van Atteveldt W, Benoit K (2017) Text analysis in R, Communication Methods and Measures, 11(4):245\u2013265. https:\/\/doi.org\/10.1080\/19312458.2017.1387238","DOI":"10.1080\/19312458.2017.1387238"},{"issue":"3","key":"7299_CR2","doi-asserted-by":"publisher","first-page":"790","DOI":"10.1177\/08944393211053743","volume":"41","author":"W Zhang","year":"2022","unstructured":"Zhang W, Hu L, Park J (2022) Viral: A Computational Text Analysis of the Public Attribution and Attitude Regarding the COVID-19 Crisis and Governmental Responses on Twitter. 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