SOTAVerified

Feature Engineering

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.

Papers

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Showing 76–100 of 1706 papers

TitleStatusHype
Feature Engineering Approach to Building Load Prediction: A Case Study for Commercial Building Chiller Plant Optimization in Tropical Weather—0
EssayJudge: A Multi-Granular Benchmark for Assessing Automated Essay Scoring Capabilities of Multimodal Large Language Models—0
SEM-CLIP: Precise Few-Shot Learning for Nanoscale Defect Detection in Scanning Electron Microscope Image—0
PainDECOG: Machine Learning-Based Identification of Pain Biomarkers from sEEG Signals—0
Recent Advances in Malware Detection: Graph Learning and Explainability—0
Chronic Diseases Prediction Using ML—0
LLM4GNAS: A Large Language Model Based Toolkit for Graph Neural Architecture Search—0
A Survey on Data-Centric AI: Tabular Learning from Reinforcement Learning and Generative AI Perspective—0
Decision Tree Based Wrappers for Hearing Loss—0
Exploring Patterns Behind Sports—0
Enhancing Physics-Informed Neural Networks Through Feature Engineering—0
Application of quantum machine learning using quantum kernel algorithms on multiclass neuron M type classification—0
Agentic AI Systems Applied to tasks in Financial Services: Modeling and model risk management crews—0
Decision Trees That Remember: Gradient-Based Learning of Recurrent Decision Trees with Memory—0
From Features to Transformers: Redefining Ranking for Scalable Impact—0
Benchmarking Time Series Forecasting Models: From Statistical Techniques to Foundation Models in Real-World Applications—0
Year-over-Year Developments in Financial Fraud Detection via Deep Learning: A Systematic Literature Review—0
CAAT-EHR: Cross-Attentional Autoregressive Transformer for Multimodal Electronic Health Record EmbeddingsCode0
RAINER: A Robust Ensemble Learning Grid Search-Tuned Framework for Rainfall Patterns Prediction—0
360Brew: A Decoder-only Foundation Model for Personalized Ranking and Recommendation—0
Sample-Efficient Behavior Cloning Using General Domain Knowledge—0
A Transferable Physics-Informed Framework for Battery Degradation Diagnosis, Knee-Onset Detection and Knee Prediction—0
EvoGP: A GPU-accelerated Framework for Tree-based Genetic ProgrammingCode7
Distributed Multi-Head Learning Systems for Power Consumption Prediction—0
DLinear-based Prediction of Remaining Useful Life of Lithium-Ion Batteries: Feature Engineering through Explainable Artificial Intelligence—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified