Federated Automated Feature Engineering

2024-12-05 · Tom Overman, Diego Klabjan

Automated feature engineering (AutoFE) is used to automatically create new features from original features to improve predictive performance without needing significant human intervention and domain expertise. Many algorithms exist for AutoFE, but very few approaches exist for the federated learning (FL) setting where data is gathered across many clients and is not shared between clients or a central server. We introduce AutoFE algorithms for the horizontal, vertical, and hybrid FL settings, which differ in how the data is gathered across clients. To the best of our knowledge, we are the first to develop AutoFE algorithms for the horizontal and hybrid FL cases, and we show that the downstream test scores of our federated AutoFE algorithms is close in performance to the case where data is held centrally and AutoFE is performed centrally.

📄 PDF Abstract BibTeX arXiv:2412.04404

Code (0)

등록된 구현이 없습니다.

Tasks

Automated Feature EngineeringFeature Engineering Federated Learning

Learned Feature Importance Scores for Automated Feature Engineering

2024-06-06 · Yihe Dong, Sercan Arik, Nathanael Yoder, Tomas Pfister

Feature engineering has demonstrated substantial utility for many machine learning workflows, such as in the small data regime or when distribution shifts are severe. Thus automating this capability can relieve much manu…

Automated Feature Engineering Feature Engineering Feature Importance Time Series

Automating Feature Engineering

2016-01-01 · NIPS 2016 2016 1 · Udayan Khurana, Fatemeh Nargesian, Horst Samulowitz, Elias Khalil 외

Feature Engineering is the task of transforming the feature space in a given learning problem to improve the performance of a trained model. It is a crucial but time intensive and skillful process, involving a data scien…

Automated Feature Engineering Feature Engineering

DIFER: Differentiable Automated Feature Engineering

2020-10-17 · Guanghui Zhu, Zhuoer Xu, Xu Guo, Chunfeng Yuan 외

Feature engineering, a crucial step of machine learning, aims to extract useful features from raw data to improve data quality. In recent years, great efforts have been devoted to Automated Feature Engineering (AutoFE) t…

Automated Feature Engineering BIG-bench Machine Learning Decoder Feature Engineering

IIFE: Interaction Information Based Automated Feature Engineering

2024-09-07 · Tom Overman, Diego Klabjan, Jean Utke

Automated feature engineering (AutoFE) is the process of automatically building and selecting new features that help improve downstream predictive performance. While traditional feature engineering requires significant d…

Automated Feature Engineering Feature Engineering

LLM-FE: Automated Feature Engineering for Tabular Data with LLMs as Evolutionary Optimizers

2025-03-18 · Nikhil Abhyankar, Parshin Shojaee, Chandan K. Reddy

Automated feature engineering plays a critical role in improving predictive model performance for tabular learning tasks. Traditional automated feature engineering methods are limited by their reliance on pre-defined tra…

Automated Feature Engineering Feature Engineering feature selection