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 601–625 of 1706 papers

TitleStatusHype
High-Level Synthesis Performance Prediction using GNNs: Benchmarking, Modeling, and Advancing—0
A Brief Survey of Machine Learning Methods for Emotion Prediction using Physiological Data—0
Gated Recursive and Sequential Deep Hierarchical Encoding for Detecting Incongruent News Articles—0
Investigating and Explaining Feature and Representation Learning in Translationese Classification—0
Reconstruction of Incomplete Wildfire Data using Deep Generative ModelsCode0
Quantifying yeast colony morphologies with feature engineering from time-lapse photographyCode0
EEG Based Emotion Sensing using convolutional neural networks—0
Supervised Learning based QoE Prediction of Video Streaming in Future Networks: A Tutorial with Comparative Study—0
AutoFITS: Automatic Feature Engineering for Irregular Time SeriesCode0
A Comprehensive Analytical Survey on Unsupervised and Semi-Supervised Graph Representation Learning Methods—0
Neural Architectures for Biological Inter-Sentence Relation Extraction—0
Data Collection and Quality Challenges in Deep Learning: A Data-Centric AI Perspective—0
Zero-shot hashtag segmentation for multilingual sentiment analysisCode1
Adversarial Machine Learning In Network Intrusion Detection Domain: A Systematic Review—0
BERTMap: A BERT-based Ontology Alignment System—0
Predicting Bandwidth Utilization on Network Links Using Machine Learning—0
Two-stage Deep Stacked Autoencoder with Shallow Learning for Network Intrusion Detection System—0
User-click Modelling for Predicting Purchase Intent—0
Transfer Learning in Conversational Analysis through Reusing Preprocessing Data as Supervisors—0
Team_BUDDI at ComMA@ICON: Exploring Individual and Joint Modelling Approaches for Detecting Aggression, Communal Bias and Gender Bias—0
BP-Net: Efficient Deep Learning for Continuous Arterial Blood Pressure Estimation using PhotoplethysmogramCode1
Dimensionality Reduction of Longitudinal 'Omics Data using Modern Tensor FactorizationCode1
On the combination of graph data for assessing thin-file borrowers' creditworthiness—0
A Deep Learning Approach for Macroscopic Energy Consumption Prediction with Microscopic Quality for Electric Vehicles—0
Understanding the Dynamics of DNNs Using Graph ModularityCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified