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 276–300 of 1706 papers

TitleStatusHype
Thelxinoë: Recognizing Human Emotions Using Pupillometry and Machine Learning—0
PIPNet3D: Interpretable Detection of Alzheimer in MRI Scans—0
VCR-Graphormer: A Mini-batch Graph Transformer via Virtual ConnectionsCode1
Utilizing the LightGBM Algorithm for Operator User Credit Assessment Research—0
Enhancing Traffic Incident Management with Large Language Models: A Hybrid Machine Learning Approach for Severity Classification—0
Scheduled Knowledge Acquisition on Lightweight Vector Symbolic Architectures for Brain-Computer Interfaces—0
Automated data processing and feature engineering for deep learning and big data applications: a survey—0
DreamSampler: Unifying Diffusion Sampling and Score Distillation for Image ManipulationCode2
Ensemble learning for predictive uncertainty estimation with application to the correction of satellite precipitation products—0
The Impact of Frequency Bands on Acoustic Anomaly Detection of Machines using Deep Learning Based Model—0
Defect Detection in Tire X-Ray Images: Conventional Methods Meet Deep Structures—0
Machine Learning-Based Completions Sequencing for Well Performance OptimizationCode0
GCOF: Self-iterative Text Generation for Copywriting Using Large Language Model—0
Towards Versatile Graph Learning Approach: from the Perspective of Large Language Models—0
AdvanceSplice: Integrating N-gram one-hot encoding and ensemble modeling for enhanced accuracyCode0
From Data to Decisions: The Transformational Power of Machine Learning in Business Recommendations—0
Retrieve, Merge, Predict: Augmenting Tables with Data LakesCode1
Descriptive Kernel Convolution Network with Improved Random Walk KernelCode0
Explainable Adversarial Learning Framework on Physical Layer Secret Keys Combating Malicious Reconfigurable Intelligent Surface—0
Unraveling the Key of Machine Learning Solutions for Android Malware Detection—0
Data organization limits the predictability of binary classification—0
A Benchmark Dataset for Tornado Detection and Prediction using Full-Resolution Polarimetric Weather Radar Data—0
GeoDecoder: Empowering Multimodal Map Understanding—0
Empowering Machines to Think Like Chemists: Unveiling Molecular Structure-Polarity Relationships with Hierarchical Symbolic RegressionCode0
SMUTF: Schema Matching Using Generative Tags and Hybrid FeaturesCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified