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 826–850 of 1706 papers

TitleStatusHype
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
Cardea: An Open Automated Machine Learning Framework for Electronic Health RecordsCode1
Towards Intelligent Risk-based Customer Segmentation in Banking—0
Citcom – Citation Recommendation—0
Orthrus: A Bimodal Learning Architecture for Malware ClassificationCode0
A Physics-Informed Machine Learning Approach for Solving Heat Transfer Equation in Advanced Manufacturing and Engineering Applications—0
A Human-in-the-Loop Approach based on Explainability to Improve NTL Detection—0
DiviK: Divisive intelligent K-Means for hands-free unsupervised clustering in big biological dataCode1
ABM: an automatic supervised feature engineering method for loss based models based on group and fused lasso—0
My tweets bring all the traits to the yard: Predicting personality and relational traits in Online Social NetworksCode0
An Exponential Factorization Machine with Percentage Error Minimization to Retail Sales Forecasting—0
SYNC: A Copula based Framework for Generating Synthetic Data from Aggregated SourcesCode1
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
Spoiled for Choice? Personalized Recommendation for Healthcare Decisions: A Multi-Armed Bandit Approach—0
Patient Cohort Retrieval using Transformer Language Models—0
ERNIE at SemEval-2020 Task 10: Learning Word Emphasis Selection by Pre-trained Language Model—0
Topological Data Analysis for Portfolio Management of Cryptocurrencies—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified