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 176–200 of 1706 papers

TitleStatusHype
Wide & Deep Learning for Node ClassificationCode0
MPEC: Manifold-Preserved EEG Classification via an Ensemble of Clustering-Based Classifiers—0
LLMpatronous: Harnessing the Power of LLMs For Vulnerability Detection—0
FLARE: Feature-based Lightweight Aggregation for Robust Evaluation of IoT Intrusion Detection—0
Word Embedding Techniques for Classification of Star Ratings—0
HMPE:HeatMap Embedding for Efficient Transformer-Based Small Object Detection—0
Morphing-based Compression for Data-centric ML Pipelines—0
Beyond Glucose-Only Assessment: Advancing Nocturnal Hypoglycemia Prediction in Children with Type 1 Diabetes—0
Bringing Structure to Naturalness: On the Naturalness of ASTs—0
Boosting Relational Deep Learning with Pretrained Tabular ModelsCode0
Feature Engineering on LMS Data to Optimize Student Performance Prediction—0
Unleashing the Power of Pre-trained Encoders for Universal Adversarial Attack Detection—0
SPIO: Ensemble and Selective Strategies via LLM-Based Multi-Agent Planning in Automated Data Science—0
FeRG-LLM : Feature Engineering by Reason Generation Large Language Models—0
Embedding Domain-Specific Knowledge from LLMs into the Feature Engineering Pipeline—0
RocketPPA: Code-Level Power, Performance, and Area Prediction via LLM and Mixture of Experts—0
Feature-Enhanced Machine Learning for All-Cause Mortality Prediction in Healthcare Data—0
Asset price movement prediction using empirical mode decomposition and Gaussian mixture models—0
Machine Learning - Driven Materials Discovery: Unlocking Next-Generation Functional Materials -- A minireview—0
CoDet-M4: Detecting Machine-Generated Code in Multi-Lingual, Multi-Generator and Multi-Domain Settings—0
Applications of Large Language Model Reasoning in Feature Generation—0
Exploring LLM Agents for Cleaning Tabular Machine Learning Datasets—0
VORTEX: Challenging CNNs at Texture Recognition by using Vision Transformers with Orderless and Randomized Token EncodingsCode0
Bridging the Semantic Gap in Virtual Machine Introspection and Forensic Memory Analysis—0
YARE-GAN: Yet Another Resting State EEG-GANCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified