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 351–375 of 1706 papers

TitleStatusHype
Comparing fingers and gestures for bci control using an optimized classical machine learning decoder—0
Horseshoe-type Priors for Independent Component Estimation—0
LightGBM robust optimization algorithm based on topological data analysis—0
PathoLM: Identifying pathogenicity from the DNA sequence through the Genome Foundation ModelCode0
Retrieval-Augmented Feature Generation for Domain-Specific Classification—0
Deep Learning Domain Adaptation to Understand Physico-Chemical Processes from Fluorescence Spectroscopy Small Datasets: Application to Ageing of Olive Oil—0
Explainable AI for Comparative Analysis of Intrusion Detection ModelsCode0
Enhancing Tabular Data Optimization with a Flexible Graph-based Reinforced Exploration Strategy—0
Learned Feature Importance Scores for Automated Feature Engineering—0
Dynamic and Adaptive Feature Generation with LLM—0
Iterative Feature Boosting for Explainable Speech Emotion RecognitionCode0
Advancements in Tactile Hand Gesture Recognition for Enhanced Human-Machine Interaction—0
Transitional Uncertainty with Layered Intermediate Predictions—0
Maintaining and Managing Road Quality:Using MLP and DNN—0
Wearable-based behaviour interpolation for semi-supervised human activity recognition—0
An LSTM Feature Imitation Network for Hand Movement Recognition from sEMG SignalsCode0
Advancing Transportation Mode Share Analysis with Built Environment: Deep Hybrid Models with Urban Road Network—0
Application of Artificial Intelligence in Schizophrenia Rehabilitation Management: A Systematic Scoping Review—0
Generic Multi-modal Representation Learning for Network Traffic Analysis—0
Explainable Automatic Grading with Neural Additive Models—0
Diagnosis of Parkinson's Disease Using EEG Signals and Machine Learning Techniques: A Comprehensive Study—0
Enhancing IoT Security: A Novel Feature Engineering Approach for ML-Based Intrusion Detection Systems—0
MediFact at MEDIQA-CORR 2024: Why AI Needs a Human TouchCode0
LEMDA: A Novel Feature Engineering Method for Intrusion Detection in IoT Systems—0
Large Language Models for Networking: Workflow, Advances and Challenges—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified