Papers Feature Engineering
“Feature Engineering” 태그가 달린 논문 1,990편 · 필터 해제
RocketPPA: Code-Level Power, Performance, and Area Prediction via LLM and Mixture of Experts
This paper presents RocketPPA, a novel ultra-fast power, performance (delay), and area (PPA) estimator operating directly at the code-level abstraction using HDL code as input. The key technical innovation is its LLM-bas…
Code RepairFeature EngineeringLarge Language ModelMixture-of-Experts+1Asset price movement prediction using empirical mode decomposition and Gaussian mixture models
We investigated the use of Empirical Mode Decomposition (EMD) combined with Gaussian Mixture Models (GMM), feature engineering and machine learning algorithms to optimize trading decisions. We used five, two, and one yea…
Ensemble LearningFeature EngineeringMachine Learning - Driven Materials Discovery: Unlocking Next-Generation Functional Materials -- A minireview
The rapid advancement of machine learning and artificial intelligence (AI)-driven techniques is revolutionizing materials discovery, property prediction, and material design by minimizing human intervention and accelerat…
AutoMLBayesian OptimizationFeature EngineeringModel Selection+1NeuralFoil: An Airfoil Aerodynamics Analysis Tool Using Physics-Informed Machine Learning
NeuralFoil is an open-source Python-based tool for rapid aerodynamics analysis of airfoils, similar in purpose to XFoil. Speedups ranging from 8x to 1,000x over XFoil are demonstrated, after controlling for equivalent ac…
Feature EngineeringPhysics-informed machine learningRobust DesignUncertainty QuantificationLLM-FE: Automated Feature Engineering for Tabular Data with LLMs as Evolutionary Optimizers
Automated feature engineering plays a critical role in improving predictive model performance for tabular learning tasks. Traditional automated feature engineering methods are limited by their reliance on pre-defined tra…
Automated Feature EngineeringFeature Engineeringfeature selectionCoDet-M4: Detecting Machine-Generated Code in Multi-Lingual, Multi-Generator and Multi-Domain Settings
Large language models (LLMs) have revolutionized code generation, automating programming with remarkable efficiency. However, these advancements challenge programming skills, ethics, and assessment integrity, making the …
Code GenerationEthicsFeature EngineeringApplications of Large Language Model Reasoning in Feature Generation
Large Language Models (LLMs) have revolutionized natural language processing through their state of art reasoning capabilities. This paper explores the convergence of LLM reasoning techniques and feature generation for m…
Computational EfficiencyDomain AdaptationFeature EngineeringHallucination+5Exploring LLM Agents for Cleaning Tabular Machine Learning Datasets
High-quality, error-free datasets are a key ingredient in building reliable, accurate, and unbiased machine learning (ML) models. However, real world datasets often suffer from errors due to sensor malfunctions, data ent…
Data IntegrationFeature EngineeringVORTEX: Challenging CNNs at Texture Recognition by using Vision Transformers with Orderless and Randomized Token Encodings
Texture recognition has recently been dominated by ImageNet-pre-trained deep Convolutional Neural Networks (CNNs), with specialized modifications and feature engineering required to achieve state-of-the-art (SOTA) perfor…
Computational EfficiencyFeature EngineeringTexture ClassificationBridging the Semantic Gap in Virtual Machine Introspection and Forensic Memory Analysis
Forensic Memory Analysis (FMA) and Virtual Machine Introspection (VMI) are critical tools for security in a virtualization-based approach. VMI and FMA involves using digital forensic methods to extract information from t…
Feature EngineeringYARE-GAN: Yet Another Resting State EEG-GAN
In this study, we implement a Wasserstein GAN with Gradient Penalty (WGAN-GP) to generate multi-channel resting-state EEG data and assess the quality of the synthesized signals through both visual and feature-based evalu…
EEGFeature EngineeringGender ClassificationRepresentation LearningEfficient or Powerful? Trade-offs Between Machine Learning and Deep Learning for Mental Illness Detection on Social Media
Social media platforms provide valuable insights into mental health trends by capturing user-generated discussions on conditions such as depression, anxiety, and suicidal ideation. Machine learning (ML) and deep learning…
Computational EfficiencyFeature Engineeringfeature selectionMulti-class ClassificationIntegrating convolutional layers and biformer network with forward-forward and backpropagation training
Accurate molecular property prediction is crucial for drug discovery and computational chemistry, facilitating the identification of promising compounds and accelerating therapeutic development. Traditional machine learn…
Computational chemistryDrug DiscoveryFeature EngineeringMolecular Property Prediction+2Improving Representation Learning of Complex Critical Care Data with ICU-BERT
The multivariate, asynchronous nature of real-world clinical data, such as that generated in Intensive Care Units (ICUs), challenges traditional AI-based decision-support systems. These often assume data regularity and f…
Feature EngineeringLanguage ModelingLanguage ModellingLarge Language Model+1Mitigating Attrition: Data-Driven Approach Using Machine Learning and Data Engineering
This paper presents a novel data-driven approach to mitigating employee attrition using machine learning and data engineering techniques. The proposed framework integrates data from various human resources systems and le…
Feature EngineeringEdge Training and Inference with Analog ReRAM Technology for Hand Gesture Recognition
Tactile hand gesture recognition is a crucial task for user control in the automotive sector, where Human-Machine Interactions (HMI) demand low latency and high energy efficiency. This study addresses the challenges of p…
Feature EngineeringGesture RecognitionHand Gesture RecognitionHand-Gesture RecognitionTabulaTime: A Novel Multimodal Deep Learning Framework for Advancing Acute Coronary Syndrome Prediction through Environmental and Clinical Data Integration
Acute Coronary Syndromes (ACS), including ST-segment elevation myocardial infarctions (STEMI) and non-ST-segment elevation myocardial infarctions (NSTEMI), remain a leading cause of mortality worldwide. Traditional cardi…
Data IntegrationFeature EngineeringFeature ImportanceMultimodal Deep Learning+1A Defensive Framework Against Adversarial Attacks on Machine Learning-Based Network Intrusion Detection Systems
As cyberattacks become increasingly sophisticated, advanced Network Intrusion Detection Systems (NIDS) are critical for modern network security. Traditional signature-based NIDS are inadequate against zero-day and evolvi…
Ensemble LearningFeature EngineeringIntrusion DetectionNetwork Intrusion DetectionML-Driven Approaches to Combat Medicare Fraud: Advances in Class Imbalance Solutions, Feature Engineering, Adaptive Learning, and Business Impact
Medicare fraud poses a substantial challenge to healthcare systems, resulting in significant financial losses and undermining the quality of care provided to legitimate beneficiaries. This study investigates the use of m…
DiagnosticDimensionality ReductionFeature Engineeringfeature selection+1EssayJudge: A Multi-Granular Benchmark for Assessing Automated Essay Scoring Capabilities of Multimodal Large Language Models
Automated Essay Scoring (AES) plays a crucial role in educational assessment by providing scalable and consistent evaluations of writing tasks. However, traditional AES systems face three major challenges: (1) reliance o…
Automated Essay ScoringFeature EngineeringSentence