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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 126–150 of 1706 papers

TitleStatusHype
Vision Transformers for Efficient Indoor Pathloss Radio Map Prediction—0
Image-Based Malware Classification Using QR and Aztec Codes—0
Robust Feature Engineering Techniques for Designing Efficient Motor Imagery-Based BCI-Systems—0
RUL forecasting for wind turbine predictive maintenance based on deep learning—0
PRECISE: Pre-training Sequential Recommenders with Collaborative and Semantic Information—0
Parkinson's Disease Diagnosis Through Deep Learning: A Novel LSTM-Based Approach for Freezing of Gait Detection—0
Federated Automated Feature Engineering—0
Deep Learning in Single-Cell and Spatial Transcriptomics Data Analysis: Advances and Challenges from a Data Science Perspective—0
Comparative Performance of Machine Learning Algorithms for Early Genetic Disorder and Subclass Classification—0
Intelligent Spark Agents: A Modular LangGraph Framework for Scalable, Visualized, and Enhanced Big Data Machine Learning Workflows—0
HiCat: A Semi-Supervised Approach for Cell Type Annotation—0
An AutoML-based approach for Network Intrusion Detection—0
Understanding LLM Embeddings for Regression—0
Enhancing Molecular Design through Graph-based Topological Reinforcement Learning—0
Advancing Heatwave Forecasting via Distribution Informed-Graph Neural Networks (DI-GNNs): Integrating Extreme Value Theory with GNNs—0
Graph Neural Networks for Quantifying Compatibility Mechanisms in Traditional Chinese MedicineCode1
Is Precise Recovery Necessary? A Task-Oriented Imputation Approach for Time Series Forecasting on Variable Subset—0
What makes a good BIM design: quantitative linking between design behavior and quality—0
GPTree: Towards Explainable Decision-Making via LLM-powered Decision Trees—0
Large Language Models for Constructing and Optimizing Machine Learning Workflows: A SurveyCode0
Classification of residential and non-residential buildings based on satellite data using deep learning—0
RAGulator: Lightweight Out-of-Context Detectors for Grounded Text Generation—0
Large Language Models Orchestrating Structured Reasoning Achieve Kaggle Grandmaster Level—0
Correlation of Object Detection Performance with Visual Saliency and Depth EstimationCode0
Exploring Feature Importance and Explainability Towards Enhanced ML-Based DoS Detection in AI Systems—0
Show:102550
← PrevPage 6 of 69Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified