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Tag: classification

  • Plug-In Classification of Drift Functions in Diffusion Processes Using Neural Networks

    Plug-In Classification of Drift Functions in Diffusion Processes Using Neural Networks arXiv:2602.02791v1 Announce Type: new Abstract: We study a supervised multiclass classification problem for diffusion processes, where each class is characterized by a distinct drift function and trajectories are observed at discrete times. Extending the one-dimensional multiclass framework of Denis et al. (2024) to multidimensional…

  • Classification Imbalance as Transfer Learning

    Classification Imbalance as Transfer Learning arXiv:2601.10630v1 Announce Type: new Abstract: Classification imbalance arises when one class is much rarer than the other. We frame this setting as transfer learning under label (prior) shift between an imbalanced source distribution induced by the observed data and a balanced target distribution under which performance is evaluated. Within this…

  • MLCBART: Multilabel Classification with Bayesian Additive Regression Trees

    MLCBART: Multilabel Classification with Bayesian Additive Regression Trees arXiv:2601.08964v1 Announce Type: cross Abstract: Multilabel Classification (MLC) deals with the simultaneous classification of multiple binary labels. The task is challenging because, not only may there be arbitrarily different and complex relationships between predictor variables and each label, but associations among labels may exist even after accounting…

  • Automated Pollen Recognition in Optical and Holographic Microscopy Images

    Automated Pollen Recognition in Optical and Holographic Microscopy Images arXiv:2512.08589v1 Announce Type: cross Abstract: This study explores the application of deep learning to improve and automate pollen grain detection and classification in both optical and holographic microscopy images, with a particular focus on veterinary cytology use cases. We used YOLOv8s for object detection and MobileNetV3L…

  • Overspecified Mixture Discriminant Analysis: Exponential Convergence, Statistical Guarantees, and Remote Sensing Applications

    Overspecified Mixture Discriminant Analysis: Exponential Convergence, Statistical Guarantees, and Remote Sensing Applications arXiv:2510.27056v1 Announce Type: new Abstract: This study explores the classification error of Mixture Discriminant Analysis (MDA) in scenarios where the number of mixture components exceeds those present in the actual data distribution, a condition known as overspecification. We use a two-component Gaussian mixture…

  • Visual Pollen Classification Using CNNs and Vision Transformers

    Visual Pollen Classification Using CNNs and Vision Transformers Filling the data gap: A machine learning approach to pollen identification in ecology and biotechnology The post Visual Pollen Classification Using CNNs and Vision Transformers appeared first on Towards Data Science. Karol Struniawski Go to original source

  • Risk-averse Fair Multi-class Classification

    Risk-averse Fair Multi-class Classification arXiv:2509.05771v1 Announce Type: new Abstract: We develop a new classification framework based on the theory of coherent risk measures and systemic risk. The proposed approach is suitable for multi-class problems when the data is noisy, scarce (relative to the dimension of the problem), and the labeling might be unreliable. In the…

  • Inequalities for Optimization of Classification Algorithms: A Perspective Motivated by Diagnostic Testing

    Inequalities for Optimization of Classification Algorithms: A Perspective Motivated by Diagnostic Testing arXiv:2508.01065v1 Announce Type: new Abstract: Motivated by canonical problems in medical diagnostics, we propose and study properties of an objective function that uniformly bounds uncertainties in quantities of interest extracted from classifiers and related data analysis tools. We begin by adopting a set-theoretic…

  • Pairwise Cross-Variance Classification

    Pairwise Cross-Variance Classification Multi-class zero-shot embedding classification and error checking The post Pairwise Cross-Variance Classification appeared first on Towards Data Science. Doster Esh Go to original source

  • Fairness-aware Bayes optimal functional classification

    Fairness-aware Bayes optimal functional classification arXiv:2505.09471v1 Announce Type: new Abstract: Algorithmic fairness has become a central topic in machine learning, and mitigating disparities across different subpopulations has emerged as a rapidly growing research area. In this paper, we systematically study the classification of functional data under fairness constraints, ensuring the disparity level of the classifier…

  • Retrieval Augmented Classification: Improving Text Classification with External Knowledge

    Retrieval Augmented Classification: Improving Text Classification with External Knowledge Text Classification stands as one of the most basic yet most important applications of natural language processing. It has a vital role in many real-world applications that go from filtering unwanted emails like spam, detecting product categories or classifying user intent in a chat-bot application. The…

  • R.E.D.: Scaling Text Classification with Expert Delegation

    R.E.D.: Scaling Text Classification with Expert Delegation With the new age of problem-solving augmented by Large Language Models (LLMs), only a handful of problems remain that have subpar solutions. Most classification problems (at a PoC level) can be solved by leveraging LLMs at 70–90% Precision/F1 with just good prompt engineering techniques, as well as adaptive…

  • Online federated learning framework for classification

    Online federated learning framework for classification arXiv:2503.15210v1 Announce Type: new Abstract: In this paper, we develop a novel online federated learning framework for classification, designed to handle streaming data from multiple clients while ensuring data privacy and computational efficiency. Our method leverages the generalized distance-weighted discriminant technique, making it robust to both homogeneous and heterogeneous…

  • Micro Text Classification Based on Balanced Positive-Unlabeled Learning

    Micro Text Classification Based on Balanced Positive-Unlabeled Learning arXiv:2503.13562v1 Announce Type: new Abstract: In real-world text classification tasks, negative texts often contain a minimal proportion of negative content, which is especially problematic in areas like text quality control, legal risk screening, and sensitive information interception. This challenge manifests at two levels: at the macro level,…

  • Choosing Classification Model Evaluation Criteria

    Choosing Classification Model Evaluation Criteria Is Recall / Precision better than Sensitivity / Specificity? Continue reading on Towards Data Science » Viyaleta Apgar Go to original source

  • Satellite Image Classification with Deep Learning — Complete Project

    Satellite Image Classification with Deep Learning — Complete Project A Comprehensive Guide Using PyTorch and CNNs Continue reading on Towards Data Science » Leo Anello Go to original source

  • Machine Learning + openAI: solving a text classification problem

    Machine Learning + openAI: solving a text classification problem How I migrated an old solution to a more elegant, robust and scalable solution using text classification from openAI Continue reading on Towards Data Science » Ricardo Ribas Go to original source