arXiv Machine Learning
Jul 27
A Leakage-Free Stacked Ensemble Method for Multiclass Classification
arXiv:2607. 22081v1 Announce Type: new Abstract: Multiclass classification is a fundamental problem across a wide range of domains.
Ensemble Learning Based Classification Algorithm Recommendation
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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arXiv Machine Learning
Jun 17
Meta-classification of one-class classification models using ranking correlation and nearest neighbor
arXiv:2606. 17858v1 Announce Type: new Abstract: Machine Learning (ML) techniques have been applied to various problems.
arXiv Machine Learning
Jun 2
LeAP: Learnable Adaptive Permutation for Feature Selection in Heterogeneous and Sparse Recommender Systems
arXiv:2606. 01111v1 Announce Type: new Abstract: Modern industrial recommender systems rely on thousands of heterogeneous features -- ranging from low-dimensional scalars (e.
arXiv Machine Learning
Jul 31
Adaptive Weighted LSSVM for Multi-View Classification
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.
arXiv Machine Learning
Jul 15
MLPTR-CC: Multi-label Pathology Test Recommendation using Classifier Chains and SHAP
arXiv:2607. 08299v2 Announce Type: replace Abstract: Diagnostic decision making often relies on a sequence of pathology tests that bridge patient symptoms and final disease diagnosis.
arXiv Machine Learning
Jul 30
Surrogate assisted diversity estimation in neural ensemble search
arXiv:2607. 26940v1 Announce Type: new Abstract: Ensembles are a standard way to improve the performance and robustness of deep neural networks, but their effectiveness crucially depends on both the quality and the diversity of individual models.
Hugging Face Trending Papers
Jul 29
Surrogate assisted diversity estimation in neural ensemble search
Ensembles are a standard way to improve the performance and robustness of deep neural networks, but their effectiveness crucially depends on both the quality and the diversity of individual models. Most neural architecture search (NAS) methods are computationally expensive.
arXiv Machine Learning
Jun 30
A Kernel Fisher Discriminant Analysis-Based Tree Ensemble Classifier: KFDA Forest
arXiv:2606. 29053v1 Announce Type: new Abstract: In general, an ensemble classifier is more accurate than a single classifier.
arXiv Machine Learning
Jul 7
Choosing a parallel heterogeneous ensemble method for tabular classification
arXiv:2607. 05103v1 Announce Type: new Abstract: Parallel ensemble methods were compared on $56$ small-to-medium tabular classification tasks drawn from OpenML CC18.
arXiv Machine Learning
Jun 8
Bradley-Terry Rankings for Recommender Systems Across Dataset Taxonomies
arXiv:2606. 07492v1 Announce Type: cross Abstract: The ranking of recommendation algorithms is a challenging problem since model performance is sensitive to dataset characteristics such as sparsity, sequential structure, and scale.
arXiv Machine Learning
Jul 7
Beyond Modality Fusion: Deep Ensembles for Multimodal Classification
arXiv:2607. 05019v1 Announce Type: new Abstract: In multimodal classification, late-fusion approaches classify concatenated modality-specific features extracted by unimodal neural networks.
arXiv AI
Jul 13
A Coreset Selection Framework with Ensemble Aggregation for Image Classification
arXiv:2607. 09100v1 Announce Type: cross Abstract: The rapid growth of image data has produced large-scale datasets, raising concerns about the time and memory costs of model training.