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 551–575 of 1706 papers

TitleStatusHype
Human heuristics for AI-generated language are flawed—0
Fault Diagnosis of Inter-turn Short Circuit in Permanent Magnet Synchronous Motors with Current Signal Imaging and Unsupervised Learning—0
SubStrat: A Subset-Based Strategy for Faster AutoMLCode0
PEER: A Comprehensive and Multi-Task Benchmark for Protein Sequence UnderstandingCode1
UMUTextStats: A linguistic feature extraction tool for Spanish—0
Pre-trained Models or Feature Engineering: The Case of Dialectal Arabic—0
OmniXAI: A Library for Explainable AICode2
Towards Context-Aware Neural Performance-Score Synchronisation—0
Group-wise Reinforcement Feature Generation for Optimal and Explainable Representation Space Reconstruction—0
Interpretable Feature Engineering for Time Series Predictors using Attention Networks—0
A Hybrid Model for Forecasting Short-Term Electricity Demand—0
Learning latent representations for operational nitrogen response rate prediction—0
A Survey on Semantics in Automated Data Science—0
On the Importance of Architecture and Feature Selection in Differentially Private Machine Learning—0
TaDeR: A New Task Dependency Recommendation for Project Management Platform—0
Compatible deep neural network framework with financial time series data, including data preprocessor, neural network model and trading strategyCode1
A simple framework for contrastive learning phases of matter—0
Are Quantum Computers Practical Yet? A Case for Feature Selection in Recommender Systems using Tensor NetworksCode0
On Designing Data Models for Energy Feature Stores—0
Joint Study of Above Ground Biomass and Soil Organic Carbon for Total Carbon Estimation using Satellite Imagery in ScotlandCode0
SeqNet: An Efficient Neural Network for Automatic Malware Detection—0
A Deep Learning Ensemble Framework for Off-Nadir Geocentric Pose PredictionCode0
Unsupervised Representation Learning of Player Behavioral Data with Confidence Guided MaskingCode0
On Machine Learning-Driven Surrogates for Sound Transmission Loss SimulationsCode0
Heterogeneous Information Network based Default Analysis on Banking Micro and Small Enterprise Users—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified