Tootfinder

Should Decision-Makers Reveal Classifiers in Online Strategic Classification?
Han Shao, Shuo Xie, Kunhe Yang
https://arxiv.org/abs/2506.01936 https://

Should Decision-Makers Reveal Classifiers in Online Strategic Classification?
Strategic classification addresses a learning problem where a decision-maker implements a classifier over agents who may manipulate their features in order to receive favorable predictions. In the standard model of online strategic classification, in each round, the decision-maker implements and publicly reveals a classifier, after which agents perfectly best respond based on this knowledge. However, in practice, whether to disclose the classifier is often debated -- some decision-makers believ…

On the Effect of Ruleset Tuning and Data Imbalance on Explainable Network Security Alert Classifications: a Case-Study on DeepCASE
Koen T. W. Teuwen, Sam Baggen, Emmanuele Zambon, Luca Allodi
https://arxiv.org/abs/2507.01571

On the Effect of Ruleset Tuning and Data Imbalance on Explainable Network Security Alert Classifications: a Case-Study on DeepCASE
Automation in Security Operations Centers (SOCs) plays a prominent role in alert classification and incident escalation. However, automated methods must be robust in the presence of imbalanced input data, which can negatively affect performance. Additionally, automated methods should make explainable decisions. In this work, we evaluate the effect of label imbalance on the classification of network intrusion alerts. As our use-case we employ DeepCASE, the state-of-the-art method for automated a…

Applying Large Language Models to Issue Classification: Revisiting with Extended Data and New Models
Gabriel Aracena, Kyle Luster, Fabio Santos, Igor Steinmacher, Marco A. Gerosa
https://arxiv.org/abs/2506.00128

Applying Large Language Models to Issue Classification: Revisiting with Extended Data and New Models
Effective prioritization of issue reports in software engineering helps to optimize resource allocation and information recovery. However, manual issue classification is laborious and lacks scalability. As an alternative, many open source software (OSS) projects employ automated processes for this task, yet this method often relies on large datasets for adequate training. Traditionally, machine learning techniques have been used for issue classification. More recently, large language models (LL…

Classifying weak Fano toric varieties of Picard rank $3$
Zhengning Hu, Rohan Joshi
https://arxiv.org/abs/2506.00715 https://arxiv.org…

Classifying weak Fano toric varieties of Picard rank $3$
We provide a systematic method to classify all smooth weak Fano toric varieties of Picard rank $3$ in any dimension using Macaulay2, and describe the classification explicitly in dimensions $3$ and $4$. There are $28$ and $114$ isomorphism classes of rank $3$ weak Fano toric threefolds and fourfolds, respectively.

Utilizing AI for Aviation Post-Accident Analysis Classification
Aziida Nanyonga, Graham Wild
https://arxiv.org/abs/2506.00169 https://

Utilizing AI for Aviation Post-Accident Analysis Classification
The volume of textual data available in aviation safety reports presents a challenge for timely and accurate analysis. This paper examines how Artificial Intelligence (AI) and, specifically, Natural Language Processing (NLP) can automate the process of extracting valuable insights from this data, ultimately enhancing aviation safety. The paper reviews ongoing efforts focused on the application of NLP and deep learning to aviation safety reports, with the goal of classifying the level of damage …

This https://arxiv.org/abs/2112.13738 has been replaced.
link: https://scholar.google.com/scholar?q=a

Evaluation of binary classifiers for asymptotically dependent and independent extremes
Machine learning classification methods usually assume that all possible classes are sufficiently present within the training set. Due to their inherent rarities, extreme events are always under-represented and classifiers tailored for predicting extremes need to be carefully designed to handle this under-representation. In this paper, we address the question of how to assess and compare classifiers with respect to their capacity to capture extreme occurrences. This is also related to the topic…

Tunable Wavelet Unit based Convolutional Neural Network in Optical Coherence Tomography Analysis Enhancement for Classifying Type of Epiretinal Membrane Surgery
An Le, Nehal Mehta, William Freeman, Ines Nagel, Melanie Tran, Anna Heinke, Akshay Agnihotri, Lingyun Cheng, Dirk-Uwe Bartsch, Hung Nguyen, Truong Nguyen, Cheolhong An
https://

Tunable Wavelet Unit based Convolutional Neural Network in Optical Coherence Tomography Analysis Enhancement for Classifying Type of Epiretinal Membrane Surgery
In this study, we developed deep learning-based method to classify the type of surgery performed for epiretinal membrane (ERM) removal, either internal limiting membrane (ILM) removal or ERM-alone removal. Our model, based on the ResNet18 convolutional neural network (CNN) architecture, utilizes postoperative optical coherence tomography (OCT) center scans as inputs. We evaluated the model using both original scans and scans preprocessed with energy crop and wavelet denoising, achieving 72% acc…

A Hybrid Ensemble Learning Framework for Image-Based Solar Panel Classification
Vivek Tetarwal, Sandeep Kumar
https://arxiv.org/abs/2507.01778 https://

A Hybrid Ensemble Learning Framework for Image-Based Solar Panel Classification
The installation of solar energy systems is on the rise, and therefore, appropriate maintenance techniques are required to be used in order to maintain maximum performance levels. One of the major challenges is the automated discrimination between clean and dirty solar panels. This paper presents a novel Dual Ensemble Neural Network (DENN) to classify solar panels using image-based features. The suggested approach utilizes the advantages offered by various ensemble models by integrating them in…

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PatchDEMUX: A Certifiably Robust Framework for Multi-label Classifiers Against Adversarial Patches
Dennis Jacob, Chong Xiang, Prateek Mittal
https://arxiv.org/abs/2505.24703

PatchDEMUX: A Certifiably Robust Framework for Multi-label Classifiers Against Adversarial Patches
Deep learning techniques have enabled vast improvements in computer vision technologies. Nevertheless, these models are vulnerable to adversarial patch attacks which catastrophically impair performance. The physically realizable nature of these attacks calls for certifiable defenses, which feature provable guarantees on robustness. While certifiable defenses have been successfully applied to single-label classification, limited work has been done for multi-label classification. In this work, we…