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 671–680 of 1706 papers

TitleStatusHype
Grasp-and-Lift Detection from EEG Signal Using Convolutional Neural Network—0
Review of automated time series forecasting pipelines—0
Compactness Score: A Fast Filter Method for Unsupervised Feature Selection—0
Automated Feature Extraction on AsMap for Emotion Classification using EEG—0
Systematic Investigation of Strategies Tailored for Low-Resource Settings for Low-Resource Dependency ParsingCode0
Learnable Wavelet Packet Transform for Data-Adapted Spectrograms—0
Exploiting Meta-Cognitive Features for a Machine-Learning-Based One-Shot Group-Decision Aggregation—0
Analyzing Multispectral Satellite Imagery of South American Wildfires Using Deep Learning—0
High-Level Synthesis Performance Prediction using GNNs: Benchmarking, Modeling, and Advancing—0
A Brief Survey of Machine Learning Methods for Emotion Prediction using Physiological Data—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified