arXiv:2607. 24145v1 Announce Type: new Abstract: Feature selection aims to identify the most informative and relevant features for a given dataset, either in terms of capturing the underlying data structure and distribution better, or with respect to the performance on a downstream task.
By Muhammad Rajabinasab, Arthur Zimek
The paper argues that many newly proposed unsupervised feature selection methods are evaluated only against other methods without a proper baseline. Using random feature selection as a baseline, the authors demonstrate that several state‑of‑the‑art methods are actually outperformed in both performance and efficiency. They call for the inclusion of random selection as a baseline and for the development of better evaluation metrics to ensure realistic assessment of future methods.
By Muhammad Rajabinasab, Michael E. Houle, Oussama Chelly, Arthur Zimek
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.
By Zahra Nabizadeh_Shahre_Babak, Farzaneh Koohestani, Nader Karimi, Shahram Shirani, Shadrokh Samavi
arXiv:2608.12057v4 Announce Type: replace
Abstract: Feature selection is one of the most important and fundamental tasks in data mining, tackled by a family of methods with an established set of eval...
By Hafiz Saud Arshad, Muhammad Rajabinasab, Arthur Zimek
arXiv:2611.07741v1 Announce Type: new
Abstract: Classification and feature learning are two closely interrelated fundamental tasks. Feature learning in supervised settings is typically achieved by tr...
By Qiufu Li, Huibin Xiao, Linlin Shen, Weijia Fan, Xi Jia, Yongdong Huang
The paper introduces a novel Siamese network framework that integrates Group‑Lasso for feature selection, aiming to identify compact feature subsets that preserve intrinsic data representations while maintaining high classification performance. Two variants are proposed—one using Euclidean distance and another employing a modified Jaccard similarity for real‑valued vectors—to better handle high‑dimensional sparse data. Experiments on 12 benchmark datasets with feature dimensions up to 7,070 demonstrate that the method consistently selects discriminative features and achieves competitive or superior classification results across eight classifiers, with embedding quality evaluated via UMAP and t‑SNE.
By Nisargo Ranjan Pal, Pradeep Singh