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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 626–650 of 1706 papers

TitleStatusHype
Interpreting Machine Learning Models for Room Temperature Prediction in Non-domestic BuildingsCode1
End-to-End Optimized Arrhythmia Detection Pipeline using Machine Learning for Ultra-Edge DevicesCode1
Precise Learning of Source Code Contextual Semantics via Hierarchical Dependence Structure and Graph Attention Networks—0
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
ICL’s Submission to the WMT21 Critical Error Detection Shared Task—0
A Hybrid Rule-Based and Neural Coreference Resolution System with an Evaluation on Dutch LiteratureCode1
Knowledge-driven Site Selection via Urban Knowledge Graph—0
Classification of fetal compromise during labour: signal processing and feature engineering of the cardiotocograph—0
OMASGAN: Out-of-Distribution Minimum Anomaly Score GAN for Sample Generation on the BoundaryCode1
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
Structural Characterization for Dialogue DisentanglementCode1
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
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified