Papers feature selection
“feature selection” 태그가 달린 논문 2,971편 · 필터 해제
mNARX+: A surrogate model for complex dynamical systems using manifold-NARX and automatic feature selection
We propose an automatic approach for manifold nonlinear autoregressive with exogenous inputs (mNARX) modeling that leverages the feature-based structure of functional-NARX (F-NARX) modeling. This novel approach, termed m…
feature selectionInterpretable Bayesian Tensor Network Kernel Machines with Automatic Rank and Feature Selection
Tensor Network (TN) Kernel Machines speed up model learning by representing parameters as low-rank TNs, reducing computation and memory use. However, most TN-based Kernel methods are deterministic and ignore parameter un…
feature selectionUncertainty QuantificationVariational InferenceLightweight Model for Poultry Disease Detection from Fecal Images Using Multi-Color Space Feature Optimization and Machine Learning
Poultry farming is a vital component of the global food supply chain, yet it remains highly vulnerable to infectious diseases such as coccidiosis, salmonellosis, and Newcastle disease. This study proposes a lightweight m…
Computational EfficiencyDimensionality Reductionfeature selectionGPUFrom Motion to Meaning: Biomechanics-Informed Neural Network for Explainable Cardiovascular Disease Identification
Cardiac diseases are among the leading causes of morbidity and mortality worldwide, which requires accurate and timely diagnostic strategies. In this study, we introduce an innovative approach that combines deep learning…
DiagnosticExplainable artificial intelligencefeature selectionImage RegistrationVulnerability Disclosure through Adaptive Black-Box Adversarial Attacks on NIDS
Adversarial attacks, wherein slight inputs are carefully crafted to mislead intelligent models, have attracted increasing attention. However, a critical gap persists between theoretical advancements and practical applica…
Change Point Detectionfeature selectionTowards Interpretable and Efficient Feature Selection in Trajectory Datasets: A Taxonomic Approach
Trajectory analysis is not only about obtaining movement data, but it is also of paramount importance in understanding the pattern in which an object moves through space and time, as well as in predicting its next move. …
Explainable artificial intelligencefeature selectionscMamba: A Scalable Foundation Model for Single-Cell Multi-Omics Integration Beyond Highly Variable Feature Selection
The advent of single-cell multi-omics technologies has enabled the simultaneous profiling of diverse omics layers within individual cells. Integrating such multimodal data provides unprecedented insights into cellular id…
BenchmarkingContrastive Learningfeature selectionMalware Classification Leveraging NLP & Machine Learning for Enhanced Accuracy
This paper investigates the application of natural language processing (NLP)-based n-gram analysis and machine learning techniques to enhance malware classification. We explore how NLP can be used to extract and analyze …
Classificationfeature selectionMalware ClassificationEfficient Malware Detection with Optimized Learning on High-Dimensional Features
Malware detection using machine learning requires feature extraction from binary files, as models cannot process raw binaries directly. A common approach involves using LIEF for raw feature extraction and the EMBER vecto…
Computational EfficiencyDimensionality Reductionfeature selectionMalware DetectionSimple is what you need for efficient and accurate medical image segmentation
While modern segmentation models often prioritize performance over practicality, we advocate a design philosophy prioritizing simplicity and efficiency, and attempted high performance segmentation model design. This pape…
feature selectionImage SegmentationMedical Image SegmentationModel Compression+3Condition Monitoring with Machine Learning: A Data-Driven Framework for Quantifying Wind Turbine Energy Loss
Wind energy significantly contributes to the global shift towards renewable energy, yet operational challenges, such as Leading-Edge Erosion on wind turbine blades, notably reduce energy output. This study introduces an …
Anomaly Detectionfeature selectionA Lightweight IDS for Early APT Detection Using a Novel Feature Selection Method
An Advanced Persistent Threat (APT) is a multistage, highly sophisticated, and covert form of cyber threat that gains unauthorized access to networks to either steal valuable data or disrupt the targeted network. These t…
Explainable artificial intelligenceExplainable Artificial Intelligence (XAI)feature selectionIntrusion DetectionOn feature selection in double-imbalanced data settings: a Random Forest approach
Feature selection is a critical step in high-dimensional classification tasks, particularly under challenging conditions of double imbalance, namely settings characterized by both class imbalance in the response variable…
feature selectionVariable SelectionOptimizing Genetic Algorithms with Multilayer Perceptron Networks for Enhancing TinyFace Recognition
This study conducts an empirical examination of MLP networks investigated through a rigorous methodical experimentation process involving three diverse datasets: TinyFace, Heart Disease, and Iris. Study Overview: The stu…
Dimensionality ReductionFeature Engineeringfeature selectionImproving Oral Cancer Outcomes Through Machine Learning and Dimensionality Reduction
Oral cancer presents a formidable challenge in oncology, necessitating early diagnosis and accurate prognosis to enhance patient survival rates. Recent advancements in machine learning and data mining have revolutionized…
DiagnosticDimensionality ReductionEnsemble Learningfeature selection+1CaliciBoost: Performance-Driven Evaluation of Molecular Representations for Caco-2 Permeability Prediction
Caco-2 permeability serves as a critical in vitro indicator for predicting the oral absorption of drug candidates during early-stage drug discovery. To enhance the accuracy and efficiency of computational predictions, we…
AutoMLDiversityDrug DiscoveryFeature Importance+1Evaluation of Machine Learning Models in Student Academic Performance Prediction
This research investigates the use of machine learning methods to forecast students' academic performance in a school setting. Students' data with behavioral, academic, and demographic details were used in implementation…
feature selectionScalable unsupervised feature selection via weight stability
Unsupervised feature selection is critical for improving clustering performance in high-dimensional data, where irrelevant features can obscure meaningful structure. In this work, we introduce the Minkowski weighted $k$-…
feature selectionPermutation-Free High-Order Interaction Tests
Kernel-based hypothesis tests offer a flexible, non-parametric tool to detect high-order interactions in multivariate data, beyond pairwise relationships. Yet the scalability of such tests is limited by the computational…
Causal Discoveryfeature selectionNoise-Resistant Label Reconstruction Feature Selection for Partial Multi-Label Learning
The "Curse of dimensionality" is prevalent across various data patterns, which increases the risk of model overfitting and leads to a decline in model classification performance. However, few studies have focused on this…
feature selectionMulti-Label Learning