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 801–825 of 1706 papers

TitleStatusHype
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
Classification of Periodic Variable Stars with Novel Cyclic-Permutation Invariant Neural NetworksCode1
Seoul bike trip duration prediction using data mining techniquesCode0
Representation learning of writing styleCode1
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
Replay and Synthetic Speech Detection with Res2net ArchitectureCode1
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
Profiling Entity Matching Benchmark TasksCode0
DIFER: Differentiable Automated Feature EngineeringCode1
Deep Learning Head Model for Real-time Estimation of Entire Brain Deformation in Concussion—0
VEST: Automatic Feature Engineering for ForecastingCode1
Credit card fraud detection using machine learning: A survey—0
Fantastic Features and Where to Find Them: Detecting Cognitive Impairment with a Subsequence Classification Guided Approach—0
A Neurochaos Learning Architecture for Genome ClassificationCode0
Escalation Prediction using Feature Engineering: Addressing Support Ticket Escalations within IBM's Ecosystem—0
Downsampling and geometric feature methods for EEG classification tasks with CNNs—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified