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 651–675 of 1706 papers

TitleStatusHype
Approaches to Fraud Detection on Credit Card Transactions Using Artificial Intelligence Methods—0
Aiding Long-Term Investment Decisions with XGBoost Machine Learning Model—0
A Deep Learning Approach for Macroscopic Energy Consumption Prediction with Microscopic Quality for Electric Vehicles—0
A Comparison of Word2Vec, HMM2Vec, and PCA2Vec for Malware Classification—0
A Blockchain Transaction Graph based Machine Learning Method for Bitcoin Price Prediction—0
IISE PG&E Energy Analytics Challenge 2025: Hourly-Binned Regression Models Beat Transformers in Load Forecasting—0
Citcom – Citation Recommendation—0
CIDMP: Completely Interpretable Detection of Malaria Parasite in Red Blood Cells using Lower-dimensional Feature Space—0
Chronic Diseases Prediction Using ML—0
Chinese Zero Pronoun Resolution with Deep Neural Networks—0
Intelligent Approaches to Predictive Analytics in Occupational Health and Safety in India—0
Chinese Zero Pronoun Resolution with Deep Memory Network—0
Chinese Semantic Role Labeling with Bidirectional Recurrent Neural Networks—0
Applications of Large Language Model Reasoning in Feature Generation—0
Chinese Grammatical Error Diagnosis Based on CRF and LSTM-CRF model—0
Chinese Event Extraction Using DeepNeural Network with Word Embedding—0
Application Research On Real-Time Perception Of Device Performance Status—0
A Hybrid Quantum Classical Pipeline for X Ray Based Fracture Diagnosis—0
Chemical-Induced Disease Detection Using Invariance-based Pattern Learning Model—0
Application of Statistical Relational Learning to Hybrid Recommendation Systems—0
Chemellia: An Ecosystem for Atomistic Scientific Machine Learning—0
Application of quantum machine learning using quantum kernel algorithms on multiclass neuron M type classification—0
A Hybrid Model for Forecasting Short-Term Electricity Demand—0
Integrating LLM, EEG, and Eye-Tracking Biomarker Analysis for Word-Level Neural State Classification in Semantic Inference Reading Comprehension—0
Application of Multi-channel 3D-cube Successive Convolution Network for Convective Storm Nowcasting—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified