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 826–850 of 1706 papers

TitleStatusHype
An Embedding Learning Framework for Numerical Features in CTR PredictionCode0
Semantic Annotation for Tabular Data—0
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
CyberTronics at SemEval-2020 Task 12: Multilingual Offensive Language Identification over Social MediaCode0
Classifying Malware Using Function Representations in a Static Call Graph—0
Neural Automated Essay Scoring Incorporating Handcrafted Features—0
BertAA : BERT fine-tuning for Authorship Attribution—0
Leveraging Latent Representations of Speech for Indian Language Identification—0
A Decade Survey of Content Based Image Retrieval using Deep Learning—0
A Worrying Analysis of Probabilistic Time-series Models for Sales Forecasting—0
Ollivier persistent Ricci curvature (OPRC) based molecular representation for drug designCode0
A Time-Frequency based Suspicious Activity Detection for Anti-Money Laundering—0
TLab: Traffic Map Movie Forecasting Based on HR-NET—0
Morphological Disambiguation from Stemming Data—0
Combining Automatic Labelers and Expert Annotations for Accurate Radiology Report Labeling Using BERT—0
Efficient Learning of Control Policies for Robust Quadruped Bounding using Pretrained Neural Networks—0
Seoul bike trip duration prediction using data mining techniquesCode0
A Survey on Churn Analysis—0
CLRGaze: Contrastive Learning of Representations for Eye Movement SignalsCode0
Deep Neural Mobile Networking—0
Online Conversation Disentanglement with Pointer Networks—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified