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 626–650 of 1706 papers

TitleStatusHype
Few-shot incremental learning in the context of solar cell quality inspection—0
Helsinki-NLP at SemEval-2022 Task 2: A Feature-Based Approach to Multilingual Idiomaticity Detection—0
Plumeria at SemEval-2022 Task 6: Sarcasm Detection for English and Arabic Using Transformers and Data Augmentation—0
Using Person Embedding to Enrich Features and Data Augmentation for Classification—0
A multi-model-based deep learning framework for short text multiclass classification with the imbalanced and extremely small data set—0
Vibration fault detection in wind turbines based on normal behaviour models without feature engineering—0
Energy reconstruction for large liquid scintillator detectors with machine learning techniques: aggregated features approach—0
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
Pre-trained Models or Feature Engineering: The Case of Dialectal Arabic—0
UMUTextStats: A linguistic feature extraction tool for Spanish—0
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
A simple framework for contrastive learning phases of matter—0
On Designing Data Models for Energy Feature Stores—0
Are Quantum Computers Practical Yet? A Case for Feature Selection in Recommender Systems using Tensor NetworksCode0
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
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified