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 101–125 of 1706 papers

TitleStatusHype
Large Language Models for Automated Data Science: Introducing CAAFE for Context-Aware Automated Feature EngineeringCode1
Attention-Based Deep Learning Framework for Human Activity Recognition with User AdaptationCode1
Anomaly Detection for Solder Joints Using β-VAECode1
Discovering Neural WiringsCode1
A Survey of Information Cascade Analysis: Models, Predictions, and Recent AdvancesCode1
Network Analytics for Anti-Money Laundering -- A Systematic Literature Review and Experimental EvaluationCode1
Automated Website Fingerprinting through Deep LearningCode1
Optimized Feature Generation for Tabular Data via LLMs with Decision Tree ReasoningCode1
Predicting crop yields with little ground truth: A simple statistical model for in-season forecastingCode1
PTRAIL -- A python package for parallel trajectory data preprocessingCode1
An End-to-End Reinforcement Learning Approach for Job-Shop Scheduling Problems Based on Constraint ProgrammingCode1
Representation learning of writing styleCode1
Online learning techniques for prediction of temporal tabular datasets with regime changesCode1
Self-supervised learning for tool wear monitoring with a disentangled-variational-autoencoderCode1
Short-term Renewable Energy Forecasting in Greece using Prophet Decomposition and Tree-based EnsemblesCode1
Simplified DOM Trees for Transferable Attribute Extraction from the WebCode1
SkillGPT: a RESTful API service for skill extraction and standardization using a Large Language ModelCode1
SMUTF: Schema Matching Using Generative Tags and Hybrid FeaturesCode1
Supervised Learning on Relational Databases with Graph Neural NetworksCode1
Symbolic regression for scientific discovery: an application to wind speed forecastingCode1
Blending gradient boosted trees and neural networks for point and probabilistic forecasting of hierarchical time seriesCode1
Synerise at RecSys 2021: Twitter user engagement prediction with a fast neural modelCode1
The Remarkable Robustness of LLMs: Stages of Inference?Code1
Deep Dive into Hunting for LotLs Using Machine Learning and Feature Engineering.Code1
General-Purpose User Embeddings based on Mobile App UsageCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified