Meta-Learning for Classifier Selection in Image Datasets: A Feature-Driven Framework for Accuracy Prediction
Updated 52 min ago · first seen 11 Sept 2026
paper_01M294FQH7S5NK3WJA8BTJ30DH
- Published
- 11 Sept 2026
- T1 · 53 min ago
- arXiv
- 2609.11041
- T1 · 53 min ago
- Category
- cs.CV
- T1 · 53 min ago
Abstract
No Free Lunch theorem implies that any performance gains achieved by a classifier on a particular image distribution are necessarily offset by a loss of performance over the set of all possible problems; thus, no single model is universally optimal. Selecting the most suitable classifier for image datasets is a critical yet challenging task due to the intrinsic complexity and diversity of images. This paper proposes a meta-learning framework that leverages a comprehensive set of meta-features capturing dataset complexity to predict classifier performance without exhaustive training. By extracting and selecting features using methods such as autoencoders, pre-trained networks, and dimensionality reduction techniques, we train regression models to efficiently estimate classifier accuracies. Additionally, clustering techniques are employed to group classifiers with similar performance patterns, simplifying the recommendation process. The datasets used span a wide range of concepts, including nature, animals, numbers, motorcycles, medical images, and human bodies, to ensure broad generalization. Evaluated on 56 diverse image datasets, our approach achieves an average ranking prediction accuracy exceeding 86%, demonstrating its effectiveness in guiding model selection. This scalable and interpretable framework provides a practical solution to improve classification performance while reducing computational costs.
Authors 5
Zahra Nabizadeh_Shahre_Babak, Farzaneh Koohestani, Nader Karimi, Shahram Shirani, Shadrokh Samavi
Specification
- Official page
Source:arXiv (Atom API + RSS)T1observed 53 min agohigh
- Arxiv announce type
- new
Source:arXiv (Atom API + RSS)T1observed 52 min agohigh
- arXiv id
- 2609.11041
Source:arXiv (Atom API + RSS)T1observed 53 min agohigh
- Categories
- cs.CV, cs.LG
Source:arXiv (Atom API + RSS)T1observed 53 min agohigh
Source:arXiv (Atom API + RSS)T1observed 53 min agohigh
- Primary category
- cs.CV
Source:arXiv (Atom API + RSS)T1observed 53 min agohigh
- Published
- 11 Sept 2026
Source:arXiv (Atom API + RSS)T1observed 53 min agohigh
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52 min ago
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- Property changedPaperMeta-Learning for Classifier Selection in Image Datasets: A Feature-Driven Framework for Accuracy Prediction
Meta-Learning for Classifier Selection in Image Datasets: A Feature-Driven Framework for Accuracy Prediction: arxiv announce type changed from cross to new
Arxiv announce typecross→newarxiv - New paperPaperMeta-Learning for Classifier Selection in Image Datasets: A Feature-Driven Framework for Accuracy Prediction
New paper: Meta-Learning for Classifier Selection in Image Datasets: A Feature-Driven Framework for Accuracy Prediction
arxiv
| Source | Document | Type | Tier | Last observed | Snapshots |
|---|---|---|---|---|---|
| arXiv (Atom API + RSS) | rss.arxiv.org/rss/cs.CV | feed | T1· Official | 52 min ago | 1 |
| arXiv (Atom API + RSS) | rss.arxiv.org/rss/cs.LG | feed | T1· Official | 53 min ago | 1 |
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