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 776–800 of 1706 papers

TitleStatusHype
Shape-based Feature Engineering for Solar Flare Prediction—0
Explainable Multi-class Classification of Medical Data—0
AutonoML: Towards an Integrated Framework for Autonomous Machine LearningCode0
Intelligent Vector-based Customer Segmentation in the Banking Industry—0
Unboxing Engagement in YouTube Influencer Videos: An Attention-Based Approach—0
Machine Learning for Detecting Data Exfiltration: A Review—0
Clinical Temporal Relation Extraction with Probabilistic Soft Logic Regularization and Global InferenceCode1
An Embedding Learning Framework for Numerical Features in CTR PredictionCode0
Semantic Annotation for Tabular Data—0
Binary Black-box Evasion Attacks Against Deep Learning-based Static Malware Detectors with Adversarial Byte-Level Language ModelCode1
Enabling Collaborative Data Science Development with the Ballet FrameworkCode1
Yelp Review Rating Prediction: Machine Learning and Deep Learning ModelsCode1
Repurposing recidivism models for forecasting police officer use of forceCode0
3D Bounding Box Detection in Volumetric Medical Image Data: A Systematic Literature Review—0
AI-enabled Prediction of eSports Player Performance Using the Data from Heterogeneous SensorsCode0
RotNet: Fast and Scalable Estimation of Stellar Rotation Periods Using Convolutional Neural Networks—0
A Novel Approach to Radiometric IdentificationCode0
Leveraging Latent Representations of Speech for Indian Language Identification—0
BertAA : BERT fine-tuning for Authorship Attribution—0
Neural Automated Essay Scoring Incorporating Handcrafted Features—0
CyberTronics at SemEval-2020 Task 12: Multilingual Offensive Language Identification over Social MediaCode0
Classifying Malware Using Function Representations in a Static Call Graph—0
CodeCMR: Cross-Modal Retrieval For Function-Level Binary Source Code MatchingCode1
Can Q-Learning with Graph Networks Learn a Generalizable Branching Heuristic for a SAT Solver?Code1
Short-Term Load Forecasting using Bi-directional Sequential Models and Feature Engineering for Small DatasetsCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified