图像数据集中分类器选择的元学习：一种基于特征驱动的精度预测框架
Meta-Learning for Classifier Selection in Image Datasets: A Feature-Driven Framework for Accuracy Prediction
浏览论文内容
中文总结 AI 辅助
提出一种基于元特征的元学习框架，通过回归模型预测分类器精度，在56个图像数据集上实现超过86%的排名预测准确率，以低成本指导模型选择。
中文摘要 AI 辅助
“没有免费午餐”定理意味着，任何分类器在特定图像分布上取得的性能提升，必然会在所有可能问题的集合上以性能损失为代价；因此，不存在普遍最优的单一模型。由于图像内在的复杂性和多样性，为图像数据集选择最合适的分类器是一项关键但具有挑战性的任务。本文提出了一种元学习框架，利用一组全面的、捕捉数据集复杂性的元特征，在无需穷举训练的情况下预测分类器性能。通过使用自编码器、预训练网络和降维技术等方法提取和选择特征，我们训练回归模型以高效估计分类器精度。此外，采用聚类技术将具有相似性能模式的分类器分组，简化推荐过程。所使用的数据集涵盖广泛的概念，包括自然、动物、数字、摩托车、医学图像和人体等，以确保广泛的泛化性。在56个多样化的图像数据集上评估，我们的方法实现了超过86%的平均排名预测精度，证明了其在指导模型选择方面的有效性。这一可扩展且可解释的框架提供了一种实用解决方案，可在降低计算成本的同时提高分类性能。
英文摘要
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.
发表机构
- McMaster University（麦克马斯特大学）
- Isfahan University of Technology（伊斯法罕理工大学）
- Seattle University（西雅图大学）
机构由 AI 辅助整理，请以论文原文为准。
