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 651–700 of 1706 papers

TitleStatusHype
A Deep Learning Ensemble Framework for Off-Nadir Geocentric Pose PredictionCode0
On Machine Learning-Driven Surrogates for Sound Transmission Loss SimulationsCode0
Unsupervised Representation Learning of Player Behavioral Data with Confidence Guided MaskingCode0
Heterogeneous Information Network based Default Analysis on Banking Micro and Small Enterprise Users—0
Automated detection of dark patterns in cookie banners: how to do it poorly and why it is hard to do it any other way—0
FenceNet: Fine-grained Footwork Recognition in Fencing—0
Deep Learning for Effective and Efficient Reduction of Large Adaptation Spaces in Self-Adaptive Systems—0
MLPro: A System for Hosting Crowdsourced Machine Learning Challenges for Open-Ended Research Problems—0
Meta-Learning Approaches for a One-Shot Collective-Decision Aggregation: Correctly Choosing how to Choose Correctly—0
Adaptive Spike-Like Representation of EEG Signals for Sleep Stages Scoring—0
i-Razor: A Differentiable Neural Input Razor for Feature Selection and Dimension Search in DNN-Based Recommender SystemsCode0
What's the Difference? The potential for Convolutional Neural Networks for transient detection without template subtractionCode0
A Machine Learning Approach to Digital Contact Tracing: TC4TL Challenge—0
Plumeria at SemEval-2022 Task 6: Robust Approaches for Sarcasm Detection for English and Arabic Using Transformers and Data AugmentationCode0
A streamable large-scale clinical EEG dataset for Deep Learning—0
Improving Performance of Automated Essay Scoring by using back-translation essays and adjusted scores—0
Multi-Layer Perceptron Neural Network for Improving Detection Performance of Malicious Phishing URLs Without Affecting Other Attack Types Classification—0
Numeric Encoding Options with AutomungeCode0
Parsed Categoric Encodings with AutomungeCode0
Vital Node Identification in Complex Networks Using a Machine Learning-Based Approach—0
Grasp-and-Lift Detection from EEG Signal Using Convolutional Neural Network—0
Review of automated time series forecasting pipelines—0
Compactness Score: A Fast Filter Method for Unsupervised Feature Selection—0
Automated Feature Extraction on AsMap for Emotion Classification using EEG—0
Systematic Investigation of Strategies Tailored for Low-Resource Settings for Low-Resource Dependency ParsingCode0
Learnable Wavelet Packet Transform for Data-Adapted Spectrograms—0
Exploiting Meta-Cognitive Features for a Machine-Learning-Based One-Shot Group-Decision Aggregation—0
Analyzing Multispectral Satellite Imagery of South American Wildfires Using Deep Learning—0
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
Reconstruction of Incomplete Wildfire Data using Deep Generative ModelsCode0
Investigating and Explaining Feature and Representation Learning in Translationese Classification—0
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
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
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
Precise Learning of Source Code Contextual Semantics via Hierarchical Dependence Structure and Graph Attention Networks—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified