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 251–275 of 1706 papers

TitleStatusHype
Analysis of Rhythmic Phrasing: Feature Engineering vs. Representation Learning for Classifying Readout Poetry—0
A Comparison of Word2Vec, HMM2Vec, and PCA2Vec for Malware Classification—0
Analyzing Multispectral Satellite Imagery of South American Wildfires Using Deep Learning—0
Attention-based Recurrent Convolutional Neural Network for Automatic Essay Scoring—0
Bayesian Kernel Methods for Natural Language Processing—0
Benchmarking Graph Neural Networks for Document Layout Analysis in Public Affairs—0
Intelligent Approaches to Predictive Analytics in Occupational Health and Safety in India—0
A Comparison of SVM against Pre-trained Language Models (PLMs) for Text Classification Tasks—0
Applications of Large Language Model Reasoning in Feature Generation—0
Application Research On Real-Time Perception Of Device Performance Status—0
A Hybrid Quantum Classical Pipeline for X Ray Based Fracture Diagnosis—0
A Benchmark Dataset for Tornado Detection and Prediction using Full-Resolution Polarimetric Weather Radar Data—0
Application of Statistical Relational Learning to Hybrid Recommendation Systems—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
Application of Multi-channel 3D-cube Successive Convolution Network for Convective Storm Nowcasting—0
Application of Machine Learning in Stock Market Forecasting: A Case Study of Disney Stock—0
A Hybrid Distribution Feeder Long-Term Load Forecasting Method Based on Sequence Prediction—0
A Deep Convolutional Neural Network for Background Subtraction—0
A Worrying Analysis of Probabilistic Time-series Models for Sales Forecasting—0
A Hybrid Approach for Smart Alert Generation—0
Application of federated learning techniques for arrhythmia classification using 12-lead ECG signals—0
A Deep Belief Network Based Machine Learning System for Risky Host Detection—0
Application of Clinical Concept Embeddings for Heart Failure Prediction in UK EHR data—0
Application of Artificial Intelligence in Schizophrenia Rehabilitation Management: A Systematic Scoping Review—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified