Papers Feature Engineering
“Feature Engineering” 태그가 달린 논문 1,990편 · 필터 해제
ClustRecNet: A Novel End-to-End Deep Learning Framework for Clustering Algorithm Recommendation
Identifying an effective clustering algorithm for a given dataset remains a fundamental unsupervised learning issue. We introduce ClustRecNet, a novel end-to-end deep learning framework that recommends suitable clusterin…
Feature EngineeringText-Based Approaches to Item Difficulty Modeling in Large-Scale Assessments: A Systematic Review
Item difficulty plays a crucial role in test performance, interpretability of scores, and equity for all test-takers, especially in large-scale assessments. Traditional approaches to item difficulty modeling rely on fiel…
Feature EngineeringEnhancing Molecular Property Prediction with Knowledge from Large Language Models
Predicting molecular properties is a critical component of drug discovery. Recent advances in deep learning, particularly Graph Neural Networks (GNNs), have enabled end-to-end learning from molecular structures, reducing…
Molecular Property PredictionSelf-Supervised LearningFeature EngineeringDrug DiscoveryIntelligent Algorithm Selection for Recommender Systems: Meta-Learning via in-depth algorithm feature engineering
The "No Free Lunch" theorem dictates that no single recommender algorithm is optimal for all users, creating a significant Algorithm Selection Problem. Standard meta-learning approaches aim to solve this by selecting an …
Feature EngineeringDynamic Lagging for Time-Series Forecasting in E-Commerce Finance: Mitigating Information Loss with A Hybrid ML Architecture
Accurate forecasting in the e-commerce finance domain is particularly challenging due to irregular invoice schedules, payment deferrals, and user-specific behavioral variability. These factors, combined with sparse datas…
Feature EngineeringBiLCNet : BiLSTM-Conformer Network for Encrypted Traffic Classification with 5G SA Physical Channel Records
Accurate and efficient traffic classification is vital for wireless network management, especially under encrypted payloads and dynamic application behavior, where traditional methods such as port-based identification an…
Feature EngineeringModeling the Attack: Detecting AI-Generated Text by Quantifying Adversarial Perturbations
The growth of highly advanced Large Language Models (LLMs) constitutes a huge dual-use problem, making it necessary to create dependable AI-generated text detection systems. Modern detectors are notoriously vulnerable to…
Adversarial RobustnessSemantic SimilarityFeature EngineeringText DetectionMulti-level Diagnosis and Evaluation for Robust Tabular Feature Engineering with Large Language Models
Recent advancements in large language models (LLMs) have shown promise in feature engineering for tabular data, but concerns about their reliability persist, especially due to variability in generated outputs. We introdu…
Feature EngineeringLow-Cost Sensor Fusion Framework for Organic Substance Classification and Quality Control Using Classification Methods
We present a sensor-fusion framework for rapid, non-destructive classification and quality control of organic substances, built on a standard Arduino Mega 2560 microcontroller platform equipped with three commercial envi…
Dimensionality ReductionFeature EngineeringHybrid Quantum-Classical Neural Networks for Few-Shot Credit Risk Assessment
Quantum Machine Learning (QML) offers a new paradigm for addressing complex financial problems intractable for classical methods. This work specifically tackles the challenge of few-shot credit risk assessment, a critica…
Quantum Machine LearningDimensionality ReductionFeature EngineeringHausaMovieReview: A Benchmark Dataset for Sentiment Analysis in Low-Resource African Language
The development of Natural Language Processing (NLP) tools for low-resource languages is critically hindered by the scarcity of annotated datasets. This paper addresses this fundamental challenge by introducing HausaMovi…
Feature EngineeringSentiment AnalysisAutomated Generation of Research Workflows from Academic Papers: A Full-text Mining Framework
The automated generation of research workflows is essential for improving the reproducibility of research and accelerating the paradigm of "AI for Science". However, existing methods typically extract merely fragmented p…
Feature EngineeringFew-Shot LearningFraming AI System Benchmarking as a Learning Task: FlexBench and the Open MLPerf Dataset
Existing AI system benchmarks such as MLPerf often struggle to keep pace with the rapidly evolving AI landscape, making it difficult to support informed deployment, optimization, and co-design decisions for AI systems. W…
Feature EngineeringPISA: An AI Pipeline for Interpretable-by-design Survival Analysis Providing Multiple Complexity-Accuracy Trade-off Models
Survival analysis is central to clinical research, informing patient prognoses, guiding treatment decisions, and optimising resource allocation. Accurate time-to-event predictions not only improve quality of life but als…
Feature EngineeringAnti-Money Laundering Machine Learning Pipelines; A Technical Analysis on Identifying High-risk Bank Clients with Supervised Learning
Anti-money laundering (AML) actions and measurements are among the priorities of financial institutions, for which machine learning (ML) has shown to have a high potential. In this paper, we propose a comprehensive and s…
Feature EngineeringFeature ImportanceTowards Adaptive ML Benchmarks: Web-Agent-Driven Construction, Domain Expansion, and Metric Optimization
Recent advances in large language models (LLMs) have enabled the emergence of general-purpose agents for automating end-to-end machine learning (ML) workflows, including data analysis, feature engineering, model training…
Feature EngineeringAn Information-Theoretic Framework for Credit Risk Modeling: Unifying Industry Practice with Statistical Theory for Fair and Interpretable Scorecards
Credit risk modeling relies extensively on Weight of Evidence (WoE) and Information Value (IV) for feature engineering, and Population Stability Index (PSI) for drift monitoring, yet their theoretical foundations remain …
Feature EngineeringFrom Limited Data to Rare-event Prediction: LLM-powered Feature Engineering and Multi-model Learning in Venture Capital
This paper presents a framework for predicting rare, high-impact outcomes by integrating large language models (LLMs) with a multi-model machine learning (ML) architecture. The approach combines the predictive strength o…
Feature EngineeringLAMDAS: LLM as an Implicit Classifier for Domain-specific Data Selection
Adapting large language models (LLMs) to specific domains often faces a critical bottleneck: the scarcity of high-quality, human-curated data. While large volumes of unchecked data are readily available, indiscriminately…
Computational EfficiencyFeature EngineeringTGLF-WINN: Data-Efficient Deep Learning Surrogate for Turbulent Transport Modeling in Fusion
The Trapped Gyro-Landau Fluid (TGLF) model provides fast, accurate predictions of turbulent transport in tokamaks, but whole device simulations requiring thousands of evaluations remain computationally expensive. Neural …
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