Papers Feature Engineering
“Feature Engineering” 태그가 달린 논문 1,990편 · 필터 해제
Empowering Time Series Forecasting with LLM-Agents
Large Language Model (LLM) powered agents have emerged as effective planners for Automated Machine Learning (AutoML) systems. While most existing AutoML approaches focus on automating feature engineering and model archit…
Time Series ForecastingFeature EngineeringData-Driven Spectrum Demand Prediction: A Spatio-Temporal Framework with Transfer Learning
Accurate spectrum demand prediction is crucial for informed spectrum allocation, effective regulatory planning, and fostering sustainable growth in modern wireless communication networks. It supports governmental efforts…
Feature EngineeringTransfer LearningByteGen: A Tokenizer-Free Generative Model for Orderbook Events in Byte Space
Generative modeling of high-frequency limit order book (LOB) dynamics is a critical yet unsolved challenge in quantitative finance, essential for robust market simulation and strategy backtesting. Existing approaches are…
Feature EngineeringReflecSched: Solving Dynamic Flexible Job-Shop Scheduling via LLM-Powered Hierarchical Reflection
The NP-hard Dynamic Flexible Job-Shop Scheduling (DFJSP) problem involves real-time events and complex routing. While traditional rules are efficient but rigid, deep learning is opaque and requires feature engineering. L…
Feature EngineeringCSLRConformer: A Data-Centric Conformer Approach for Continuous Arabic Sign Language Recognition on the Isharah Datase
The field of Continuous Sign Language Recognition (CSLR) poses substantial technical challenges, including fluid inter-sign transitions, the absence of temporal boundaries, and co-articulation effects. This paper, develo…
Sign Language RecognitionFeature EngineeringSpeech RecognitionMARS: A Meta-Adaptive Reinforcement Learning Framework for Risk-Aware Multi-Agent Portfolio Management
Reinforcement Learning (RL) has shown significant promise in automated portfolio management; however, effectively balancing risk and return remains a central challenge, as many models fail to adapt to dynamically changin…
Reinforcement LearningFeature EngineeringForecasting NCAA Basketball Outcomes with Deep Learning: A Comparative Study of LSTM and Transformer Models
In this research, I explore advanced deep learning methodologies to forecast the outcomes of the 2025 NCAA Division 1 Men's and Women's Basketball tournaments. Leveraging historical NCAA game data, I implement two sophis…
Feature EngineeringEfficient Real-Time Aircraft ETA Prediction via Feature Tokenization Transformer
Estimated time of arrival (ETA) for airborne aircraft in real-time is crucial for arrival management in aviation, particularly for runway sequencing. Given the rapidly changing airspace context, the ETA prediction effici…
Feature EngineeringGUARD-CAN: Graph-Understanding and Recurrent Architecture for CAN Anomaly Detection
Modern in-vehicle networks face various cyber threats due to the lack of encryption and authentication in the Controller Area Network (CAN). To address this security issue, this paper presents GUARD-CAN, an anomaly detec…
Representation LearningFeature EngineeringAnomaly DetectionGraph EmbeddingExplainability-Driven Feature Engineering for Mid-Term Electricity Load Forecasting in ERCOT's SCENT Region
Accurate load forecasting is essential to the operation of modern electric power systems. Given the sensitivity of electricity demand to weather variability and temporal dynamics, capturing non-linear patterns is essenti…
Feature EngineeringAgent0: Leveraging LLM Agents to Discover Multi-value Features from Text for Enhanced Recommendations
Large language models (LLMs) and their associated agent-based frameworks have significantly advanced automated information extraction, a critical component of modern recommender systems. While these multitask frameworks …
Information ExtractionFeature EngineeringCode GenerationFD4QC: Application of Classical and Quantum-Hybrid Machine Learning for Financial Fraud Detection A Technical Report
The increasing complexity and volume of financial transactions pose significant challenges to traditional fraud detection systems. This technical report investigates and compares the efficacy of classical, quantum, and q…
Quantum Machine LearningBinary ClassificationFeature EngineeringFraud DetectionAccelerating HEC-RAS: A Recurrent Neural Operator for Rapid River Forecasting
Physics-based solvers like HEC-RAS provide high-fidelity river forecasts but are too computationally intensive for on-the-fly decision-making during flood events. The central challenge is to accelerate these simulations …
Feature EngineeringSelective Embedding for Deep Learning
Deep learning has revolutionized many industries by enabling models to automatically learn complex patterns from raw data, reducing dependence on manual feature engineering. However, deep learning algorithms are sensitiv…
Computational EfficiencyFeature EngineeringA Comprehensive Analysis of Churn Prediction in Telecommunications Using Machine Learning
Customer churn prediction in the telecommunications sector represents a critical business intelligence task that has evolved from subjective human assessment to sophisticated algorithmic approaches. In this work, we pres…
Feature EngineeringPlayer-Team Heterogeneous Interaction Graph Transformer for Soccer Outcome Prediction
Predicting soccer match outcomes is a challenging task due to the inherently unpredictable nature of the game and the numerous dynamic factors influencing results. While it conventionally relies on meticulous feature eng…
Feature EngineeringVisual Analytics for Explainable and Trustworthy Artificial Intelligence
Our society increasingly depends on intelligent systems to solve complex problems, ranging from recommender systems suggesting the next movie to watch to AI models assisting in medical diagnoses for hospitalized patients…
Feature EngineeringImproving Remote Sensing Classification using Topological Data Analysis and Convolutional Neural Networks
Topological data analysis (TDA) is a relatively new field that is gaining rapid adoption due to its robustness and ability to effectively describe complex datasets by quantifying geometric information. In imaging context…
Scene ClassificationFeature EngineeringAdvanced Health Misinformation Detection Through Hybrid CNN-LSTM Models Informed by the Elaboration Likelihood Model (ELM)
Health misinformation during the COVID-19 pandemic has significantly challenged public health efforts globally. This study applies the Elaboration Likelihood Model (ELM) to enhance misinformation detection on social medi…
Feature EngineeringA Novel Hybrid Deep Learning Technique for Speech Emotion Detection using Feature Engineering
Nowadays, speech emotion recognition (SER) plays a vital role in the field of human-computer interaction (HCI) and the evolution of artificial intelligence (AI). Our proposed DCRF-BiLSTM model is used to recognize seven …
Speech Emotion RecognitionFeature Engineering