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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 1601–1650 of 1706 papers

TitleStatusHype
A Survey on Recent Advances in Named Entity Recognition from Deep Learning modelsCode0
Fast and Accurate Neural Word Segmentation for ChineseCode0
Reproducible Machine Learning-based Voice Pathology Detection: Introducing the Pitch Difference FeatureCode0
Repurposing recidivism models for forecasting police officer use of forceCode0
Novelty Goes Deep. A Deep Neural Solution To Document Level Novelty DetectionCode0
Convolutional Neural Network with Word Embeddings for Chinese Word SegmentationCode0
Application of Machine Learning in Rock Facies Classification with Physics-Motivated Feature AugmentationCode0
Context-Based Tweet Engagement PredictionCode0
Numeric Encoding Options with AutomungeCode0
Responsive and Self-Expressive Dialogue GenerationCode0
Feature Interaction Aware Automated Data Representation TransformationCode0
YARE-GAN: Yet Another Resting State EEG-GANCode0
A Surprising Thing: The Application of Machine Learning Ensembles and Signal Theory to Predict Earnings SurprisesCode0
Ollivier persistent Ricci curvature (OPRC) based molecular representation for drug designCode0
Feature Engineering and Forecasting via Derivative-free Optimization and Ensemble of Sequence-to-sequence Networks with Applications in Renewable EnergyCode0
Automated Treatment Planning in Radiation Therapy using Generative Adversarial NetworksCode0
Solving the "false positives" problem in fraud predictionCode0
AutoM3L: An Automated Multimodal Machine Learning Framework with Large Language ModelsCode0
A Deep Learning Approach for Automatic Detection of Fake NewsCode0
Space-Time Representation of People Based on 3D Skeletal Data: A ReviewCode0
ReVal: A Simple and Effective Machine Translation Evaluation Metric Based on Recurrent Neural NetworksCode0
One Size Does Not Fit All: Multi-Scale, Cascaded RNNs for Radar ClassificationCode0
Advances in deep learning methods for pavement surface crack detection and identification with visible light visual imagesCode0
Text to Band Gap: Pre-trained Language Models as Encoders for Semiconductor Band Gap PredictionCode0
Revisiting neural relation classification in clinical notes with external informationCode0
On Machine Learning-Driven Surrogates for Sound Transmission Loss SimulationsCode0
Towards Resilient and Secure Smart Grids against PMU Adversarial Attacks: A Deep Learning-Based Robust Data Engineering ApproachCode0
On the Benefit of Combining Neural, Statistical and External Features for Fake News IdentificationCode0
Spatio-Temporal Stability Analysis in Satellite Image Times SeriesCode0
Learning Sentiment-Specific Word Embedding for Twitter Sentiment ClassificationCode0
Risk Analysis of Flowlines in the Oil and Gas Sector: A GIS and Machine Learning ApproachCode0
RiWalk: Fast Structural Node Embedding via Role IdentificationCode0
Learning Text-Image Joint Embedding for Efficient Cross-Modal Retrieval with Deep Feature EngineeringCode0
Feature Engineering with Regularity StructuresCode0
Feature engineering workflow for activity recognition from synchronized inertial measurement unitsCode0
Classification of integers based on residue classes via modern deep learning algorithmsCode0
VORTEX: Challenging CNNs at Texture Recognition by using Vision Transformers with Orderless and Randomized Token EncodingsCode0
Spectral Cross-Domain Neural Network with Soft-adaptive Threshold Spectral EnhancementCode0
The autofeat Python Library for Automated Feature Engineering and SelectionCode0
Learning to Rank Question Answer Pairs with Holographic Dual LSTM ArchitectureCode0
AdaptoML-UX: An Adaptive User-centered GUI-based AutoML Toolkit for Non-AI Experts and HCI ResearchersCode0
Condition Assessment of Stay Cables through Enhanced Time Series Classification Using a Deep Learning ApproachCode0
Feature Selection and Feature Extraction in Pattern Analysis: A Literature ReviewCode0
Malware Makeover: Breaking ML-based Static Analysis by Modifying Executable BytesCode0
Less is More: Facial Landmarks can Recognize a Spontaneous SmileCode0
A study of N-gram and Embedding Representations for Native Language IdentificationCode0
AutoLearn - Automated Feature Generation and SelectionCode0
The Catechol Benchmark: Time-series Solvent Selection Data for Few-shot Machine LearningCode0
An Empirical Study on the Usage of Automated Machine Learning ToolsCode0
Orthrus: A Bimodal Learning Architecture for Malware ClassificationCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified