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Feature Engineering

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Feature engineering is the process of taking a dataset and constructing explanatory variables — features — that can be used to train a machine learning model for a prediction problem. Often, data is spread across multiple tables and must be gathered into a single table with rows containing the observations and features in the columns.

The traditional approach to feature engineering is to build features one at a time using domain knowledge, a tedious, time-consuming, and error-prone process known as manual feature engineering. The code for manual feature engineering is problem-dependent and must be re-written for each new dataset.

논문

이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.

Advancing Magnetic Materials Discovery -- A structure-based machine learning approach for magnetic ordering and magnetic moment prediction

A Deep Learning Approach to Identify Rock Bolts in Complex 3D Point Clouds of Underground Mines Captured Using Mobile Laser Scanners

Exploring Microstructural Dynamics in Cryptocurrency Limit Order Books: Better Inputs Matter More Than Stacking Another Hidden Layer

Transformers Beyond Order: A Chaos-Markov-Gaussian Framework for Short-Term Sentiment Forecasting of Any Financial OHLC timeseries Data

LSM-2: Learning from Incomplete Wearable Sensor Data

Does Prompt Design Impact Quality of Data Imputation by LLMs?

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.

Feature Engineering

논문

Covariate-Adjusted Deep Causal Learning for Heterogeneous Panel Data Models

Missing value imputation with adversarial random forests -- MissARF

MoTM: Towards a Foundation Model for Time Series Imputation based on Continuous Modeling

Advancing Magnetic Materials Discovery -- A structure-based machine learning approach for magnetic ordering and magnetic moment prediction

Prompt Mechanisms in Medical Imaging: A Comprehensive Survey

Quantum Reinforcement Learning Trading Agent for Sector Rotation in the Taiwan Stock Market

Temporal-Aware Graph Attention Network for Cryptocurrency Transaction Fraud Detection

BMFM-DNA: A SNP-aware DNA foundation model to capture variant effects

Leveraging AI Graders for Missing Score Imputation to Achieve Accurate Ability Estimation in Constructed-Response Tests

Tabular Feature Discovery With Reasoning Type Exploration

A Deep Learning Approach to Identify Rock Bolts in Complex 3D Point Clouds of Underground Mines Captured Using Mobile Laser Scanners

DIM-SUM: Dynamic IMputation for Smart Utility Management

Trustworthy Prediction with Gaussian Process Knowledge Scores

LSCD: Lomb-Scargle Conditioned Diffusion for Time series Imputation

Covariance Decomposition for Distance Based Species Tree Estimation

Relational Deep Learning: Challenges, Foundations and Next-Generation Architectures

Enhancing Forecasting Accuracy in Dynamic Environments via PELT-Driven Drift Detection and Model Adaptation

PeakWeather: MeteoSwiss Weather Station Measurements for Spatiotemporal Deep Learning

Cross-Domain Conditional Diffusion Models for Time Series Imputation

Advanced fraud detection using machine learning models: enhancing financial transaction security