arXiv:2101. 05993v2 Announce Type: replace-cross Abstract: Selecting an appropriate classification algorithm for a given data set remains a challenging problem in data mining and machine learning.
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The paper introduces a meta‑learning framework that uses a rich set of dataset‑complexity meta‑features to predict the accuracy of different classifiers on image datasets, avoiding exhaustive training. By extracting features with autoencoders, pre‑trained networks, and dimensionality reduction, regression models estimate classifier accuracies, while clustering groups similar performers to simplify recommendations. Tested on 56 diverse image datasets, the method achieves over 86% ranking prediction accuracy, offering a scalable, interpretable solution for model selection and cost reduction.
The paper introduces a method for combining heterogeneous, allied datasets—datasets that share the same class labels but have disjoint objects and largely distinct feature spaces—into a single unified feature space. By applying matrix completion to this merged space, the authors create a unified dataset that enables knowledge transfer between the original datasets. Experiments across multiple dataset pairs and classifiers show that models trained on the unified representation consistently outperform those trained separately on each dataset.
arXiv:2607. 22081v1 Announce Type: new Abstract: Multiclass classification is a fundamental problem across a wide range of domains.
arXiv:2606. 17858v1 Announce Type: new Abstract: Machine Learning (ML) techniques have been applied to various problems.
arXiv:2606. 01111v1 Announce Type: new Abstract: Modern industrial recommender systems rely on thousands of heterogeneous features -- ranging from low-dimensional scalars (e.
arXiv:2512. 02653v2 Announce Type: replace Abstract: Multi-view learning integrates diverse representations of the same instances and can improve performance when interactions across views are effectively exploited.