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 171–180 of 1706 papers

TitleStatusHype
Machine Learning-Based Detection of DDoS Attacks in VANETs for Emergency Vehicle Communication—0
Benchmarking Graph Neural Networks for Document Layout Analysis in Public Affairs—0
QoSBERT: An Uncertainty-Aware Approach based on Pre-trained Language Models for Service Quality Prediction—0
Latte: Transfering LLMs` Latent-level Knowledge for Few-shot Tabular Learning—0
Rethinking Multimodal Sentiment Analysis: A High-Accuracy, Simplified Fusion Architecture—0
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
HMPE:HeatMap Embedding for Efficient Transformer-Based Small Object Detection—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified