k-nearest neighbors algorithm
PulseAugur coverage of k-nearest neighbors algorithm — every cluster mentioning k-nearest neighbors algorithm across labs, papers, and developer communities, ranked by signal.
- instance of alphaXiv 90%
- instance of IArxiv 90%
- instance of decision tree 90%
- instance of naive Bayes classifier 90%
- used by CatalyzeX 70%
- used by alphaXiv 70%
- instance of Gotit.pub 70%
- instance of ScienceCast 70%
- used by IArxiv 70%
- developed IArxiv 70%
- competes with logistic regression model 60%
- used by Gotit.pub 60%
5 day(s) with sentiment data
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LLM-generated heart disease rules lag traditional models in accuracy
A new study published on arXiv evaluates the effectiveness of Large Language Models (LLMs) like GPT-4o and Claude Sonnet 4.6 in generating rules for heart disease prediction. The research found that traditional machine …
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New research explores PCA and Random Forest for hyperspectral image classification
A new research paper explores methods for classifying hyperspectral satellite images by focusing on dimensionality reduction and supervised classification techniques. The study compares Principal Component Analysis (PCA…
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Machine learning models struggle with Canny edge detection for Parkinson's classification
A new study published on arXiv explores the effectiveness of machine learning models in classifying Parkinson's disease, with a particular focus on preprocessing techniques. Researchers found that while augmenting datas…
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Quantum Granular-Ball Learning Enhances ML Efficiency and Robustness
Two new research papers introduce Quantum Granular-Ball Learning (QGB-W$k$NN) and Granular-Ball Quantum Clustering (GBQC) frameworks. These methods aim to improve the efficiency and robustness of machine learning tasks,…
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Machine learning enhances power system security classification
Researchers have developed a machine learning approach to enhance power system security by classifying contingency scenarios. The study utilized algorithms like Random Forest, Support Vector Machines, and K-Nearest Neig…
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AI models accurately classify Parkinson's disease severity using sensor data
Researchers have developed a machine learning approach to classify Parkinson's disease severity using data from triaxial inertial measurement unit (IMU) sensors. The study compared several classification models, with th…
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New CARSANN method enhances nearest neighbor classification accuracy
Researchers have developed a new method called Curvature-Aware Radius Shrinkage for Adaptive Nearest Neighbor Classification (CARSANN) to improve the accuracy of nearest neighbor classification. This framework adapts th…
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New gradient-free method enables test-time adaptation for frozen AI models
Researchers have developed CASTER, a novel gradient-free method for test-time adaptation (TTA) that allows models to adapt without updating their parameters. This approach is particularly useful for inference-only accel…
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Deep learning approach improves ovarian ultrasound classification accuracy
Researchers have developed a lesion-guided region-of-interest (ROI) deep learning approach for ovarian ultrasound classification, achieving high accuracy while reducing annotation effort. This method was evaluated on tw…
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Hyperbolic geometry boosts tree-structured prototype networks
Researchers have explored the impact of latent manifold geometry on hierarchical classification models, comparing Euclidean and hyperbolic spaces. Their findings indicate that hyperbolic prototypes significantly preserv…
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Hyperbolic geometry boosts latent space topology in classification models
Researchers explored the impact of latent manifold choice on hierarchical classification models, comparing Euclidean space with hyperbolic space (Poincaré ball). Their findings indicate that hyperbolic prototypes signif…
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Ensemble AI models achieve 99.52% accuracy in stroke prediction
Researchers have developed an ensemble of convolutional neural networks designed to improve the accuracy of stroke prediction. The study evaluated seven supervised machine learning algorithms, with ensemble methods like…
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New benchmark evaluates LLM embeddings for text anomaly detection
Researchers have introduced Text-ADBench, a new benchmark designed to evaluate text anomaly detection methods. The benchmark utilizes embeddings from various large language models (LLMs) across different text datasets, …
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New SAGE-XGBoost framework boosts hazard mapping in data-scarce conditions
Researchers have developed SAGE-XGBoost, a novel machine learning framework designed to improve natural hazard susceptibility mapping, particularly in data-scarce environments. This framework integrates spatially augmen…
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New CMR-Mamba method improves industrial fault detection
Researchers have developed CMR-Mamba, a novel approach for unsupervised fault detection in industrial systems that goes beyond traditional methods focusing on individual sensor data. This new technique utilizes Mamba st…
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Deep learning framework decodes sex from prehistoric hand stencils
Researchers have developed a novel deep learning framework designed to determine the biological sex of individuals who created prehistoric hand stencils. This uncertainty-aware system addresses challenges like the lack …
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New adaptive KNN classifier uses granular ball computing for efficiency
Researchers have developed a novel adaptive k-Nearest Neighbors (KNN) classifier using granular ball computing. This method involves a two-stage process: first, the dataset is partitioned into granular balls, with the F…
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CAKE framework co-designs compiler agents for GPU kernel evolution
Researchers have developed CAKE, a novel co-design framework that integrates compiler technology with AI agents to enhance GPU kernel evolution. This system allows agents to author a specialized intermediate representat…
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New research applies dynamics models for offline hyperparameter selection in real-world RL
A new research paper explores the use of dynamics models for offline hyperparameter selection in real-world reinforcement learning (RL) applications. The study demonstrates the first application of these models in an in…
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Machine learning models show improved path loss prediction for LPWANs
Researchers have conducted a systematic analysis of machine learning models for predicting path loss in Low Power Wide Area Networks (LPWANs), specifically focusing on LoRa technology. The study employed Random Forest m…