arXiv Machine Learning

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 16

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.

By Toshitaka Hayashi, Dalibor Cimr, Hamido Fujita, Richard Cimler

arXiv Machine Learning

Jul 3

Few-Shot Open-Set Audio Classification Using Attention Information-Fused Prototypes

arXiv:2607. 01297v1 Announce Type: cross Abstract: Most existing audio classification methods suppose that each query (testing) sample belongs to a class of support (training) samples, and misrecognize samples of unseen classes as seen classes (cannot reject samples of unseen classes).

By Yanxiong Li, Jiaxin Tan, Qianqian Li, Guoqing Chen, Sen Huang, Tuomas Virtanen

rag

arXiv AI

Jun 30

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.

By Guangtao Wang, Qinbao Song, Xiaoyan Zhu, Jiao Liu

benchmarks

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 Machine Learning

Jun 25

Extreme Meta-Classification for Large-Scale Zero-Shot Retrieval

arXiv:2606. 25237v1 Announce Type: cross Abstract: We develop accurate and efficient solutions for large-scale retrieval tasks where novel (zero-shot) items can arrive continuously at a rapid pace.

By Sachin Yadav, Deepak Saini, Anirudh Buvanesh, Bhawna Paliwal, Kunal Dahiya, Siddarth Asokan, Yashoteja Prabhu, Jian Jiao, Manik Varma

arXiv Machine Learning

Aug 3

POSSE-kNN: Pathwise Out-of-Bag Selected Subspace Ensembles for Binary Classification

arXiv:2211. 11278v3 Announce Type: replace-cross Abstract: Nearest neighbour classification is attractive for tabular data, but its performance can deteriorate when a fixed query centred neighbourhood does not follow the local class geometry.

By Zardad Khan, Amjad Ali, Najd Adeed, Saeed Aldahmani

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

Jul 14

On the Necessity of Output Distribution Reweighting for Effective Class Unlearning

arXiv:2506. 20893v5 Announce Type: replace-cross Abstract: In this paper, we reveal a significant shortcoming in class unlearning evaluations: overlooking the underlying class geometry can cause information leakage about the forgotten class.

By Ali Ebrahimpour-Boroojeny, Yian Wang, Hari Sundaram

fine-tuning benchmarks safety

arXiv AI

Jun 24

Exploring Dualistic Meta-Learning to Enhance Domain Generalization in Open Set Scenarios

arXiv:2606. 23758v1 Announce Type: cross Abstract: Domain generalization learns from multiple source domains to generalize to unseen target domains.

By Xiran Wang, Jian Zhang, Lei Qi, Yang Gao, Yinghuan Shi

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.

By S. P. Sharmila, Aruna Tiwari

arXiv Machine Learning

1d ago

A Parameter-Free Few-Shot Evaluation for Elephant Vocalisation Classification

arXiv:2608. 14824v1 Announce Type: cross Abstract: We present a parameter-free episodic evaluation of nearest-centroid classification for elephant vocalisations on fixed pretrained acoustic embeddings, across the Elephant Voices (EV) and Linguistic Data Consortium (LDC) datasets.

By Christiaan M. Geldenhuys, Thomas R. Niesler

rag

arXiv Machine Learning

Jul 7

Labeled-Data-Free Meta-Learning: Efficient Task Generation Using Pre-trained Models and Unlabeled Data

arXiv:2607. 02850v1 Announce Type: new Abstract: Meta-learning without labeled data is crucial for real-world applications, where obtaining labeled datasets can be expensive or restricted due to privacy concerns.

By Lei Sun, Yusuke Tanaka, Tomoharu Iwata

benchmarks safety

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