Papers Feature Engineering
“Feature Engineering” 태그가 달린 논문 1,990편 · 필터 해제
Benchmarking Graph Neural Networks for Document Layout Analysis in Public Affairs
The automatic analysis of document layouts in digital-born PDF documents remains a challenging problem due to the heterogeneous arrangement of textual and nontextual elements and the imprecision of the textual metadata i…
BenchmarkingDocument Layout AnalysisFeature Engineeringgraph construction+1QoSBERT: An Uncertainty-Aware Approach based on Pre-trained Language Models for Service Quality Prediction
Accurate prediction of Quality of Service (QoS) metrics is fundamental for selecting and managing cloud based services. Traditional QoS models rely on manual feature engineering and yield only point estimates, offering n…
Feature EngineeringPredictionUncertainty QuantificationLatte: Transfering LLMs` Latent-level Knowledge for Few-shot Tabular Learning
Few-shot tabular learning, in which machine learning models are trained with a limited amount of labeled data, provides a cost-effective approach to addressing real-world challenges. The advent of Large Language Models (…
Feature EngineeringGeneral KnowledgeUnsupervised Pre-trainingRethinking Multimodal Sentiment Analysis: A High-Accuracy, Simplified Fusion Architecture
Multimodal sentiment analysis, a pivotal task in affective computing, seeks to understand human emotions by integrating cues from language, audio, and visual signals. While many recent approaches leverage complex attenti…
Emotion ClassificationFeature EngineeringMultimodal Sentiment AnalysisSentiment AnalysisWide & Deep Learning for Node Classification
Wide & Deep, a simple yet effective learning architecture for recommendation systems developed by Google, has had a significant impact in both academia and industry due to its combination of the memorization ability of g…
ClassificationDeep LearningFeature EngineeringMemorization+2MPEC: Manifold-Preserved EEG Classification via an Ensemble of Clustering-Based Classifiers
Accurate classification of EEG signals is crucial for brain-computer interfaces (BCIs) and neuroprosthetic applications, yet many existing methods fail to account for the non-Euclidean, manifold structure of EEG data, re…
ClassificationClusteringEEGFeature EngineeringLLMpatronous: Harnessing the Power of LLMs For Vulnerability Detection
Despite the transformative impact of Artificial Intelligence (AI) across various sectors, cyber security continues to rely on traditional static and dynamic analysis tools, hampered by high false positive rates and super…
Feature EngineeringRAGRetrieval-augmented GenerationVulnerability DetectionFLARE: Feature-based Lightweight Aggregation for Robust Evaluation of IoT Intrusion Detection
The proliferation of Internet of Things (IoT) devices has expanded the attack surface, necessitating efficient intrusion detection systems (IDSs) for network protection. This paper presents FLARE, a feature-based lightwe…
Feature EngineeringIntrusion DetectionHMPE:HeatMap Embedding for Efficient Transformer-Based Small Object Detection
Current Transformer-based methods for small object detection continue emerging, yet they have still exhibited significant shortcomings. This paper introduces HeatMap Position Embedding (HMPE), a novel Transformer Optimiz…
DecoderFeature EngineeringObjectobject-detection+2Word Embedding Techniques for Classification of Star Ratings
Telecom services are at the core of today's societies' everyday needs. The availability of numerous online forums and discussion platforms enables telecom providers to improve their services by exploring the views of the…
ClassificationDimensionality ReductionFeature Engineeringtext-classification+2Morphing-based Compression for Data-centric ML Pipelines
Data-centric ML pipelines extend traditional machine learning (ML) pipelines -- of feature transformations and ML model training -- by outer loops for data cleaning, augmentation, and feature engineering to create high-q…
Feature EngineeringBeyond Glucose-Only Assessment: Advancing Nocturnal Hypoglycemia Prediction in Children with Type 1 Diabetes
The dead-in-bed syndrome describes the sudden and unexplained death of young individuals with Type 1 Diabetes (T1D) without prior long-term complications. One leading hypothesis attributes this phenomenon to nocturnal hy…
Decision MakingFeature EngineeringModel SelectionTransfer LearningBringing Structure to Naturalness: On the Naturalness of ASTs
Source code comes in different shapes and forms. Previous research has already shown code to be more predictable than natural language as well as highlighted its statistical predictability at the token level: source code…
Feature EngineeringLanguage ModellingBoosting Relational Deep Learning with Pretrained Tabular Models
Relational databases, organized into tables connected by primary-foreign key relationships, are a common format for organizing data. Making predictions on relational data often involves transforming them into a flat tabu…
Deep LearningFeature EngineeringFeature Engineering on LMS Data to Optimize Student Performance Prediction
Nearly every educational institution uses a learning management system (LMS), often producing terabytes of data generated by thousands of people. We examine LMS grade and login data from a regional comprehensive universi…
Feature EngineeringManagementUnleashing the Power of Pre-trained Encoders for Universal Adversarial Attack Detection
Adversarial attacks pose a critical security threat to real-world AI systems by injecting human-imperceptible perturbations into benign samples to induce misclassification in deep learning models. While existing detectio…
Adversarial AttackAdversarial Attack DetectionAnomaly DetectionFeature Engineering+2FeRG-LLM : Feature Engineering by Reason Generation Large Language Models
One of the key tasks in machine learning for tabular data is feature engineering. Although it is vital for improving the performance of models, it demands considerable human expertise and deep domain knowledge, making it…
Feature EngineeringLarge Language ModelSPIO: Ensemble and Selective Strategies via LLM-Based Multi-Agent Planning in Automated Data Science
Large Language Models (LLMs) have revolutionized automated data analytics and machine learning by enabling dynamic reasoning and adaptability. While recent approaches have advanced multi-stage pipelines through multi-age…
Feature EngineeringFeature-Enhanced Machine Learning for All-Cause Mortality Prediction in Healthcare Data
Accurate patient mortality prediction enables effective risk stratification, leading to personalized treatment plans and improved patient outcomes. However, predicting mortality in healthcare remains a significant challe…
AllFeature EngineeringMortality PredictionPredictionEmbedding Domain-Specific Knowledge from LLMs into the Feature Engineering Pipeline
Feature engineering is mandatory in the machine learning pipeline to obtain robust models. While evolutionary computation is well-known for its great results both in feature selection and feature construction, its method…
Feature Engineeringfeature selectionSymbolic Regression