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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 851–875 of 1706 papers

TitleStatusHype
Profiling Entity Matching Benchmark TasksCode0
Deep Learning Head Model for Real-time Estimation of Entire Brain Deformation in Concussion—0
Credit card fraud detection using machine learning: A survey—0
Fantastic Features and Where to Find Them: Detecting Cognitive Impairment with a Subsequence Classification Guided Approach—0
A Neurochaos Learning Architecture for Genome ClassificationCode0
Escalation Prediction using Feature Engineering: Addressing Support Ticket Escalations within IBM's Ecosystem—0
Downsampling and geometric feature methods for EEG classification tasks with CNNs—0
Customer Support Ticket Escalation Prediction using Feature Engineering—0
Spatio-Temporal Stability Analysis in Satellite Image Times SeriesCode0
RealSmileNet: A Deep End-To-End Network for Spontaneous and Posed Smile Recognition—0
Early Detection of Myocardial Infarction in Low-Quality Echocardiography—0
A deep learning framework for Text-independent Writer IdentificationCode0
Towards Intelligent Risk-based Customer Segmentation in Banking—0
A Physics-Informed Machine Learning Approach for Solving Heat Transfer Equation in Advanced Manufacturing and Engineering Applications—0
Orthrus: A Bimodal Learning Architecture for Malware ClassificationCode0
Citcom – Citation Recommendation—0
A Human-in-the-Loop Approach based on Explainability to Improve NTL Detection—0
ABM: an automatic supervised feature engineering method for loss based models based on group and fused lasso—0
An Exponential Factorization Machine with Percentage Error Minimization to Retail Sales Forecasting—0
My tweets bring all the traits to the yard: Predicting personality and relational traits in Online Social NetworksCode0
Feature Engineering for Data-driven Traffic State Forecast in Urban Road Networks—0
Better Model Selection with a new Definition of Feature Importance—0
Malicious Network Traffic Detection via Deep Learning: An Information Theoretic ViewCode0
Ensemble learning of diffractive optical networks—0
A Systematic Literature Review on the Use of Deep Learning in Software Engineering Research—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified