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 701–725 of 1706 papers

TitleStatusHype
A Data-Centric Behavioral Machine Learning Platform to Reduce Health Inequalities—0
Innovative Measures of Patient and Disease Phenotyping: Optimizing Linguistic and Machine Learning Techniques in the Investigation of Electronic Health Record (EHR) Data—0
Automatic Analysis of Linguistic Features in Journal Articles of Different Academic Impacts with Feature Engineering Techniques—0
Automated PII Extraction from Social Media for Raising Privacy Awareness: A Deep Transfer Learning Approach—0
Artificial Intelligence Technology analysis using Artificial Intelligence patent through Deep Learning model and vector space model—0
Biologically Inspired Oscillating Activation Functions Can Bridge the Performance Gap between Biological and Artificial Neurons—0
Language Semantics Interpretation with an Interaction-based Recurrent Neural Networks—0
Knowledge-driven Site Selection via Urban Knowledge Graph—0
ICL’s Submission to the WMT21 Critical Error Detection Shared Task—0
Classification of fetal compromise during labour: signal processing and feature engineering of the cardiotocograph—0
Concepts for Automated Machine Learning in Smart Grid Applications—0
Merging Two Cultures: Deep and Statistical Learning—0
Learning Text-Image Joint Embedding for Efficient Cross-Modal Retrieval with Deep Feature EngineeringCode0
Robust Event Classification Using Imperfect Real-world PMU Data—0
AEFE: Automatic Embedded Feature Engineering for Categorical Features—0
Tutorial on Deep Learning for Human Activity RecognitionCode0
TEET! Tunisian Dataset for Toxic Speech Detection—0
Deep convolutional forest: a dynamic deep ensemble approach for spam detection in textCode0
Feature Imitating Networks—0
Minimal-Configuration Anomaly Detection for IIoT Sensors—0
Learning post-processing for QRS detection using Recurrent Neural Network—0
Post-hoc Models for Performance Estimation of Machine Learning Inference—0
GenTAL: Generative Denoising Skip-gram Transformer for Unsupervised Binary Code Similarity Detection—0
Automated Mobile Attention KPConv Networks via A Wide & Deep Predictor—0
Deep Learning-Based Detection of the Acute Respiratory Distress Syndrome: What Are the Models Learning?—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified