SOTAVerified

Automated Feature Engineering

Automated feature engineering improves upon the traditional approach to feature engineering by automatically extracting useful and meaningful features from a set of related data tables with a framework that can be applied to any problem.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1–46 of 46 papers

TitleStatusHype
Benchmarking Automatic Machine Learning FrameworksCode3
Layered TPOT: Speeding up Tree-based Pipeline OptimizationCode3
LLM-FE: Automated Feature Engineering for Tabular Data with LLMs as Evolutionary OptimizersCode2
Optimized Feature Generation for Tabular Data via LLMs with Decision Tree ReasoningCode1
fseval: A Benchmarking Framework for Feature Selection and Feature Ranking AlgorithmsCode1
Cardea: An Open Automated Machine Learning Framework for Electronic Health RecordsCode1
Feature Programming for Multivariate Time Series PredictionCode1
Supervised Video Summarization via Multiple Feature Sets with Parallel AttentionCode1
DIFER: Differentiable Automated Feature EngineeringCode1
Large Language Models for Automated Data Science: Introducing CAAFE for Context-Aware Automated Feature EngineeringCode1
One button machine for automating feature engineering in relational databases—0
A Systematic Literature Review on the Use of Deep Learning in Software Engineering Research—0
Automated Feature Extraction on AsMap for Emotion Classification using EEG—0
Automating Feature Engineering—0
Benchmark Performance of Machine And Deep Learning Based Methodologies for Urdu Text Document Classification—0
Cognito: Automated Feature Engineering for Supervised Learning—0
Dynamic and Adaptive Feature Generation with LLM—0
Exploiting Unsupervised Pre-training and Automated Feature Engineering for Low-resource Hate Speech Detection in Polish—0
FeatGeNN: Improving Model Performance for Tabular Data with Correlation-based Feature Extraction—0
Feature Engineering for Predictive Modeling using Reinforcement Learning—0
Feature Selection as a One-Player Game—0
Feature Selection with Distance Correlation—0
Federated Automated Feature Engineering—0
IL-Net: Using Expert Knowledge to Guide the Design of Furcated Neural Networks—0
Learned Feature Importance Scores for Automated Feature Engineering—0
Learning a Data-Driven Policy Network for Pre-Training Automated Feature Engineering—0
Learning Feature Engineering for Classification—0
Machine Learning for Detecting Data Exfiltration: A Review—0
Quantum Reinforcement Learning Trading Agent for Sector Rotation in the Taiwan Stock Market—0
Semantic-Guided RL for Interpretable Feature Engineering—0
Statistical and machine learning ensemble modelling to forecast sea surface temperature—0
Techniques for Automated Machine Learning—0
Toward Efficient Automated Feature Engineering—0
Feature Interaction Aware Automated Data Representation TransformationCode0
Catch: Collaborative Feature Set Search for Automated Feature EngineeringCode0
The autofeat Python Library for Automated Feature Engineering and SelectionCode0
AdaptoML-UX: An Adaptive User-centered GUI-based AutoML Toolkit for Non-AI Experts and HCI ResearchersCode0
ExploreKit: Automatic Feature Generation and SelectionCode0
IIFE: Interaction Information Based Automated Feature EngineeringCode0
AutonoML: Towards an Integrated Framework for Autonomous Machine LearningCode0
Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data ScienceCode0
Deep Feature Synthesis: Towards Automating Data Science EndeavorsCode0
AutoLearn - Automated Feature Generation and SelectionCode0
Towards automated feature engineering for credit card fraud detection using multi-perspective HMMsCode0
Solving the "false positives" problem in fraud predictionCode0
Lifting Interpretability-Performance Trade-off via Automated Feature EngineeringCode0
Show:102550

No leaderboard results yet.