arXiv Machine Learning

Decomposing one-class support vector machine into an ensemble of one-data support vector machines

arXiv:2606. 16002v1 Announce Type: new Abstract: One-class classification (OCC) is a classification problem in which the training data contains only one class.

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.

By Toshitaka Hayashi, Hamido Fujita, Dalibor Cimr, Richard Cimler, Jitka K\"uhnov\'a

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.

By Pedro Rocha Dantas, Lucas Pascotti Valem

computer-vision

arXiv Machine Learning

Jun 11

RCAP: Robust, Class-Aware, Probabilistic Dynamic Dataset Pruning

arXiv:2606. 11761v1 Announce Type: new Abstract: Dynamic data pruning techniques aim to reduce computational cost while minimizing information loss by periodically selecting representative subsets of input data during model training.

By Atif Hassan, Swanand Khare, Jiaul H. Paik

fine-tuning efficiency benchmarks

arXiv Machine Learning

Jun 5

Robust and sparse support vector machine via hybrid truncated loss for supervised classification

arXiv:2606. 05814v1 Announce Type: new Abstract: The support vector machine (SVM) is a widely used classifier, but choosing an appropriate loss function remains difficult.

By Yuliang Yang, Chen Chen, Yuxiang Liu, Huiru Wang

arXiv Machine Learning

Jun 19

Efficient Neural Network Model Selection for Few-Class Application Datasets

arXiv:2606. 19712v1 Announce Type: new Abstract: While much effort has focused on developing and benchmarking high-performance neural networks, less attention has been given to how dataset properties, known to practitioners, can guide efficient model selection.

By Bryan Bo Cao, Abhinav Sharma, Lawrence O'Gorman, Michael Coss, Shubham Jain

robotics benchmarks

arXiv Machine Learning

Jun 3

Combining Statistical Features and Deep Encodings for Rehearsal-Based Class-Incremental Time Series Classification

arXiv:2606. 03292v1 Announce Type: cross Abstract: Many systems used in real-world environments require adding new categories and incorporating new information without forgetting what was previously learnt by the classification model.

By Pablo Garc\'ia-Santaclara, Bruno Fern\'andez-Castro, Rebeca Pilar D\'iaz-Redondo

rag benchmarks

arXiv Machine Learning

Jul 14

Vertical Fusion: Condensing Internal Representations for Robust ViT Classification

arXiv:2607. 10391v1 Announce Type: cross Abstract: Despite exposing rich intermediate representations, Vision Transformers (ViTs) are almost exclusively utilized as black-box feature extractors, where only the last layer is considered for downstream tasks.

By Francesco Di Salvo, Shyam Nandan Rai, Hamed Damirchi, Ignacio Meza De la Jara, Sebastian Doerrich, Marco Lents, Christian Ledig

llms diffusion

arXiv Machine Learning

Jul 24

ADABORD: a novel AdaBoost approach for ordinal classification

arXiv:2607. 21003v1 Announce Type: new Abstract: Ordinal Classification (OC) deals with classification tasks where the classes follow a natural order.

By Rafael Ayll\'on-Gavil\'an, Francisco Jos\'e Mart\'inez-Estudillo, David Guijo-Rubio, C\'esar Herv\'as-Mart\'inez, Pedro A. Guti\'errez

benchmarks

arXiv AI

Jun 8

On the importance of multiple training seeds for evaluating machine unlearning

arXiv:2510. 26714v5 Announce Type: replace-cross Abstract: Machine unlearning aims to remove the influence of certain data points from a trained model without costly retraining.

By Jamie Lanyon, Axel Finke, Petros Andreou, Georgina Cosma

llms computer-vision

arXiv AI

1d ago

Hoeffding adaptive splitting trees for data stream classification with concept drift and ensemble learning

arXiv:2608. 16659v1 Announce Type: cross Abstract: Ensembles of decision trees are well-established methods for data stream classification.

By Daniel Nowak Assis, Jean Paul Barddal, Fabr\'icio Enembreck

benchmarks

arXiv AI

5d ago

Concept Drift Detection and Adaptive Retraining of Malware Classification Models

arXiv:2608. 13465v1 Announce Type: cross Abstract: Concept drift refers to changes over time in the statistical properties of data, as compared to the data that was used to train a learning model.

By Christofer Washington Berruz Chungata, Martin Jurecek, Katerina Potika, William B. Andreopoulos, Mark Stamp

arXiv Machine Learning

Jul 9

PB-OEL: A Performance-Bounded Online Ensemble Learning Framework With Mixed Feedback for Real-Time Safety Assessment

arXiv:2503. 15581v2 Announce Type: replace Abstract: Real-time safety assessment is critical for ensuring the reliable operation of complex dynamic systems.

By Songqiao Hu, Zeyi Liu, Lufeng Hao, Yinzhong Cheng, Xiao He

benchmarks safety

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