Meta-Learning for Classifier Selection in Image Datasets: A Feature-Driven Framework for Accuracy Prediction — Paper

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: arxiv.org/abs/2609.11041
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
PDF: arxiv.org/pdf/2609.11041
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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9

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T19

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52 min ago

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Authors: Zahra Nabizadeh_Shahre_Babak, Farzaneh Koohestani, Nader Karimi

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Claim history for Published
Value	Valid from → to	Status	Source	Confidence	Extractor
11 Sept 2026	11 Sept 2026 21:02 UTC → current	current	arXiv (Atom API + RSS)T1	high	deterministic

Claims are temporal and append-only: a new observation closes the previous claim (valid_to) instead of overwriting it. Conflicting claims from different sources are kept side by side and flagged — never averaged. Methodology →

11 Sept 2026
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
rss.arxiv.org
11 Sept 2026
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
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Source documents
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

Tier 1 = official/primary, 2 = quality secondary, 3 = community, 4 = unverified. Every snapshot is archived; see all sources and the methodology.