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 501–525 of 1706 papers

TitleStatusHype
Automatic Seizure Prediction using CNN and LSTM—0
Feature Engineering vs BERT on Twitter Data—0
End-to-end Ensemble-based Feature Selection for Paralinguistics Tasks—0
QUILL: Query Intent with Large Language Models using Retrieval Augmentation and Multi-stage Distillation—0
Explaining Translationese: why are Neural Classifiers Better and what do they Learn?—0
Tools for Extracting Spatio-Temporal Patterns in Meteorological Image Sequences: From Feature Engineering to Attention-Based Neural Networks—0
Feature Engineering and Classification Models for Partial Discharge in Power Transformers—0
Machine Learning for K-adaptability in Two-stage Robust OptimizationCode0
DPIS: An Enhanced Mechanism for Differentially Private SGD with Importance Sampling—0
Application of Explainable Machine Learning in Detecting and Classifying Ransomware Families Based on API Call Analysis—0
Object-Category Aware Reinforcement Learning—0
Less is More: Facial Landmarks can Recognize a Spontaneous SmileCode0
Temporal Spatial Decomposition and Fusion Network for Time Series Forecasting—0
Point Cloud Recognition with Position-to-Structure Attention Transformers—0
EM-PERSONA: EMotion-assisted Deep Neural Framework for PERSONAlity Subtyping from Suicide Notes—0
Automated Mobile Attention KPConv Networks via a Wide and Deep Predictor—0
FeatureBox: Feature Engineering on GPUs for Massive-Scale Ads Systems—0
Estimating Brain Age with Global and Local Dependencies—0
Self-Optimizing Feature Transformation—0
Prediction of the outcome of a Twenty-20 Cricket Match : A Machine Learning Approach—0
Tackling Data Drift with Adversarial Validation: An Application for German Text Complexity Estimation—0
Everybody likes short sentences - A Data Analysis for the Text Complexity DE Challenge 2022—0
Fraud Dataset Benchmark and ApplicationsCode2
Artificial Neural Networks for Finger Vein Recognition: A Survey—0
Lateral Movement Detection Using User Behavioral Analysis—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified