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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 701–750 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
ICL’s Submission to the WMT21 Critical Error Detection Shared Task—0
Knowledge-driven Site Selection via Urban Knowledge Graph—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
Automated Mobile Attention KPConv Networks via A Wide & Deep Predictor—0
GenTAL: Generative Denoising Skip-gram Transformer for Unsupervised Binary Code Similarity Detection—0
Deep Learning-Based Detection of the Acute Respiratory Distress Syndrome: What Are the Models Learning?—0
SFFDD: Deep Neural Network with Enriched Features for Failure Prediction with Its Application to Computer Disk Driver—0
Unsupervised Continual Learning in Streaming Environments—0
Feature Engineering for US State Legislative Hearings: Stance, Affiliation, Engagement and Absentees—0
Comparing Feature-Engineering and Feature-Learning Approaches for Multilingual Translationese Classification—0
Beyond Glass-Box Features: Uncertainty Quantification Enhanced Quality Estimation for Neural Machine Translation—0
A comparative study of six model complexity metrics to search for parsimonious models with GAparsimony R Package—0
Detecting Attacks on IoT Devices using Featureless 1D-CNN—0
Data Science Kitchen at GermEval 2021: A Fine Selection of Hand-Picked Features, Delivered Fresh from the OvenCode0
RF-LighGBM: A probabilistic ensemble way to predict customer repurchase behaviour in community e-commerce—0
Precog-LTRC-IIITH at GermEval 2021: Ensembling Pre-Trained Language Models with Feature EngineeringCode0
Personality Trait Identification Using the Russian Feature Extraction Toolkit—0
Time Series Prediction using Deep Learning Methods in Healthcare—0
Growing Cosine Unit: A Novel Oscillatory Activation Function That Can Speedup Training and Reduce Parameters in Convolutional Neural Networks—0
End-To-End Anomaly Detection for Identifying Malicious Cyber Behavior through NLP-Based Log Embeddings—0
Towards Personalized and Human-in-the-Loop Document Summarization—0
Data-driven Smart Ponzi Scheme Detection—0
Feature Engineering with Regularity StructuresCode0
Empirical Analysis on Effectiveness of NLP Methods for Predicting Code Smell—0
Deep Learning Chromatic and Clique Numbers of GraphsCode0
Effective Model Integration Algorithm for Improving Link and Sign Prediction in Complex Networks—0
Classification of Electrical Impedance Tomography Data Using Machine Learning—0
Efficient Deep Feature Calibration for Cross-Modal Joint Embedding Learning—0
Alejandro Mosquera at SemEval-2021 Task 1: Exploring Sentence and Word Features for Lexical Complexity Prediction—0
CLULEX at SemEval-2021 Task 1: A Simple System Goes a Long Way—0
A Plant Root System Algorithm Based on Swarm Intelligence for One-dimensional Biomedical Signal Feature Engineering—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified