Papers Feature Engineering
“Feature Engineering” 태그가 달린 논문 1,990편 · 필터 해제
FailureSensorIQ: A Multi-Choice QA Dataset for Understanding Sensor Relationships and Failure Modes
We introduce FailureSensorIQ, a novel Multi-Choice Question-Answering (MCQA) benchmarking system designed to assess the ability of Large Language Models (LLMs) to reason and understand complex, domain-specific scenarios …
BenchmarkingFeature Engineeringfeature selectionQuestion AnsweringComparing the Effects of Persistence Barcodes Aggregation and Feature Concatenation on Medical Imaging
In medical image analysis, feature engineering plays an important role in the design and performance of machine learning models. Persistent homology (PH), from the field of topological data analysis (TDA), demonstrates r…
Feature EngineeringMedical Image AnalysisTopological Data AnalysisCNN-LSTM Hybrid Model for AI-Driven Prediction of COVID-19 Severity from Spike Sequences and Clinical Data
The COVID-19 pandemic, caused by SARS-CoV-2, highlighted the critical need for accurate prediction of disease severity to optimize healthcare resource allocation and patient management. The spike protein, which facilitat…
Feature EngineeringRobust classificationseverity predictionTransforming Podcast Preview Generation: From Expert Models to LLM-Based Systems
Discovering and evaluating long-form talk content such as videos and podcasts poses a significant challenge for users, as it requires a considerable time investment. Previews offer a practical solution by providing conci…
Feature EngineeringMachine Learning Algorithm for Noise Reduction and Disease-Causing Gene Feature Extraction in Gene Sequencing Data
In this study, we propose a machine learning-based method for noise reduction and disease-causing gene feature extraction in gene sequencing DeepSeqDenoise algorithm combines CNN and RNN to effectively remove the sequenc…
Feature EngineeringAssistedDS: Benchmarking How External Domain Knowledge Assists LLMs in Automated Data Science
Large language models (LLMs) have advanced the automation of data science workflows. Yet it remains unclear whether they can critically leverage external domain knowledge as human data scientists do in practice. To answe…
BenchmarkingFeature EngineeringModel SelectionScalable and Interpretable Contextual Bandits: A Literature Review and Retail Offer Prototype
This paper presents a concise review of Contextual Multi-Armed Bandit (CMAB) methods and introduces an experimental framework for scalable, interpretable offer selection, addressing the challenge of fast-changing offers.…
Feature EngineeringLarge Language ModelMulti-Armed BanditsThompson Sampling+1Action is All You Need: Dual-Flow Generative Ranking Network for Recommendation
We introduce the Dual-Flow Generative Ranking Network (DFGR), a two-stream architecture designed for recommendation systems. DFGR integrates innovative interaction patterns between real and fake flows within the QKV modu…
AllAttributeFeature EngineeringRecommendation SystemsAgentic Feature Augmentation: Unifying Selection and Generation with Teaming, Planning, and Memories
As a widely-used and practical tool, feature engineering transforms raw data into discriminative features to advance AI model performance. However, existing methods usually apply feature selection and generation separate…
Decision MakingFeature Engineeringfeature selectionIn-Context Learning+1Enhancing Abstractive Summarization of Scientific Papers Using Structure Information
Abstractive summarization of scientific papers has always been a research focus, yet existing methods face two main challenges. First, most summarization models rely on Encoder-Decoder architectures that treat papers as …
Abstractive Text SummarizationFeature EngineeringTime to Embed: Unlocking Foundation Models for Time Series with Channel Descriptions
Traditional time series models are task-specific and often depend on dataset-specific training and extensive feature engineering. While Transformer-based architectures have improved scalability, foundation models, common…
Feature EngineeringRepresentation LearningTime SeriesDeep Learning-Based Forecasting of Boarding Patient Counts to Address ED Overcrowding
This study develops deep learning models to forecast the number of patients in the emergency department (ED) boarding phase six hours in advance, aiming to support proactive operational decision-making using only non-cli…
Feature EngineeringText embedding models can be great data engineers
Data engineering pipelines are essential - albeit costly - components of predictive analytics frameworks requiring significant engineering time and domain expertise for carrying out tasks such as data ingestion, preproce…
Feature EngineeringTime SeriesGSDFuse: Capturing Cognitive Inconsistencies from Multi-Dimensional Weak Signals in Social Media Steganalysis
The ubiquity of social media platforms facilitates malicious linguistic steganography, posing significant security risks. Steganalysis is profoundly hindered by the challenge of identifying subtle cognitive inconsistenci…
Data AugmentationFeature EngineeringLinguistic steganographySteganalysisA Hybrid Quantum Classical Pipeline for X Ray Based Fracture Diagnosis
Bone fractures are a leading cause of morbidity and disability worldwide, imposing significant clinical and economic burdens on healthcare systems. Traditional X ray interpretation is time consuming and error prone, whil…
Dimensionality ReductionFeature EngineeringTransfer LearningMachine Learning-Based Prediction of Mortality in Geriatric Traumatic Brain Injury Patients
Traumatic Brain Injury (TBI) is a major contributor to mortality among older adults, with geriatric patients facing disproportionately high risk due to age-related physiological vulnerability and comorbidities. Early and…
Decision MakingFeature EngineeringImputationInterpretable Machine Learning+1Lightweight Spatio-Temporal Attention Network with Graph Embedding and Rotational Position Encoding for Traffic Forecasting
Traffic forecasting is a key task in the field of Intelligent Transportation Systems. Recent research on traffic forecasting has mainly focused on combining graph neural networks (GNNs) with other models. However, GNNs o…
Feature EngineeringGraph EmbeddingPositionIISE PG&E Energy Analytics Challenge 2025: Hourly-Binned Regression Models Beat Transformers in Load Forecasting
Accurate electricity load forecasting is essential for grid stability, resource optimization, and renewable energy integration. While transformer-based deep learning models like TimeGPT have gained traction in time-serie…
Computational EfficiencyDeep LearningDimensionality ReductionFeature Engineering+3NeurIPS 2024 Ariel Data Challenge: Characterisation of Exoplanetary Atmospheres Using a Data-Centric Approach
The characterization of exoplanetary atmospheres through spectral analysis is a complex challenge. The NeurIPS 2024 Ariel Data Challenge, in collaboration with the European Space Agency's (ESA) Ariel mission, provided an…
Feature EngineeringMachine Learning-Based Detection of DDoS Attacks in VANETs for Emergency Vehicle Communication
Vehicular Ad Hoc Networks (VANETs) play a key role in Intelligent Transportation Systems (ITS), particularly in enabling real-time communication for emergency vehicles. However, Distributed Denial of Service (DDoS) attac…
Feature EngineeringFeature ImportanceImputation