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 526–550 of 1706 papers

TitleStatusHype
Classification of integers based on residue classes via modern deep learning algorithmsCode0
AMC-Net: An Effective Network for Automatic Modulation Classification—0
Modelling customer churn for the retail industry in a deep learning based sequential framework—0
Improving extreme weather events detection with light-weight neural networks—0
A Slow-Shifting Concerned Machine Learning Method for Short-term Traffic Flow Forecasting—0
Feature Engineering Methods on Multivariate Time-Series Data for Financial Data Science Competitions—0
Fine-Tashkeel: Finetuning Byte-Level Models for Accurate Arabic Text Diacritization—0
Une comparaison des algorithmes d'apprentissage pour la survie avec données manquantes—0
Clustering US Counties to Find Patterns Related to the COVID-19 Pandemic—0
A machine learning and feature engineering approach for the prediction of the uncontrolled re-entry of space objects—0
Large-scale End-of-Life Prediction of Hard Disks in Distributed Datacenters—0
Deep Learning for Iris Recognition: A Review—0
Learning From High-Dimensional Cyber-Physical Data Streams for Diagnosing Faults in Smart Grids—0
Deep incremental learning models for financial temporal tabular datasets with distribution shifts—0
Transferable Deep Learning Power System Short-Term Voltage Stability Assessment with Physics-Informed Topological Feature Engineering—0
A Study of Variable-Role-based Feature Enrichment in Neural Models of Code—0
Multi-Scale Control Signal-Aware Transformer for Motion Synthesis without Phase—0
IoT Device Identification Based on Network Communication Analysis Using Deep Learning—0
Machine Learning for QoS Prediction in Vehicular Communication: Challenges and Solution Approaches—0
Evaluating the Effectiveness of Pre-trained Language Models in Predicting the Helpfulness of Online Product ReviewsCode0
Streamlining models with explanations in the learning loopCode0
Self-Supervised Learning for Modeling Gamma-ray Variability in Blazars—0
Text2shape Deep Retrieval Model: Generating Initial Cases for Mechanical Part Redesign under the Context of Case-Based Reasoning—0
Scientific Computing with Diffractive Optical Neural Networks—0
A Survey on Arabic Named Entity Recognition: Past, Recent Advances, and Future Trends—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified