Tag: Feature Selection

Researchers at the University of Gondar report that a voting ensemble of tree-based machine learning models combined with permutation-based feature ...

A new stochastic fractal search-based framework pairs nature-inspired optimization with a lightweight k-nearest neighbor classifier to classify mammographic lesions with ...

Researchers in China have developed an interpretable machine learning model that analyzes all liver metastases on contrast-enhanced CT scans to ...

A comprehensive Heliyon benchmark of 16 survival prediction methods and 12 feature engineering strategies across three radiomic datasets finds that ...

Researchers in Algeria have built an optimized neural network that predicts the analgesic activity of drug-like molecules with 95.9 percent ...

A new study combining statistics and machine learning shows that measurable postural instability emerges at simulated altitudes as low as ...

A new open-source Python package called ROOFS benchmarks multiple feature selection methods on a researcher's own data, evaluating predictive performance ...

Researchers in Türkiye used small language models and retrieval-augmented web search to generate new features for tabular data classification, reaching ...

Researchers in India have developed an optimized machine learning framework combining preprocessing, feature selection and hyperparameter tuning that predicts agronomic ...

A new framework combines a platform-free federated learning blockchain consensus mechanism with optimized neural networks to secure electronic health record ...

A hybrid Transformer-BiLSTM deep learning framework with a dual nature-inspired feature selection method reports up to 99 percent accuracy in ...

Researchers at Lorestan University have combined ant colony optimization, reinforcement learning, and opposition-based learning in a semi-supervised framework that improves ...

A retrospective study found that combining a thymoma with a three-millimeter rim of surrounding tissue on CT scans improved machine ...

Researchers at the University of Calabria have developed RLAX, a reinforcement learning framework that identifies compact, interpretable feature subspaces explaining ...

Researchers have enhanced a rime-ice-inspired optimization algorithm with four adaptive strategies, achieving superior benchmark results and 95.52 percent accuracy in ...

A hybrid deep learning framework combining fuzzy preprocessing, bio-inspired feature selection and a novel Hopfield attention network predicts organic perovskite ...

Researchers have unveiled PMFS-NRDLC, a unified framework that removes noisy labels and selects informative features simultaneously for partial multi-label learning, ...

Researchers adapted a swarm intelligence algorithm modeled on great white shark hunting behavior to select the most informative genes from ...

Researchers in India report that a hybrid ADAMW plus SGD optimizer with embedded feature selection boosts deep learning accuracy for ...

A sequential hybrid metaheuristic framework combining PSO, HHO, or BSO with the Binary Al-Biruni Earth Radius algorithm produces more compact ...