Automated Feature Engineering
17 papers with code • 0 benchmarks • 0 datasets
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.
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Latest papers with no code
IL-Net: Using Expert Knowledge to Guide the Design of Furcated Neural Networks
In this work, we develop a novel furcated neural network architecture that utilizes domain knowledge as high-level design principles of the network.
Feature Engineering for Predictive Modeling using Reinforcement Learning
It involves the transformation of given feature space, typically using mathematical functions, with the objective of reducing the modeling error for a given target.
One button machine for automating feature engineering in relational databases
Feature engineering is one of the most important and time consuming tasks in predictive analytics projects.
Learning Feature Engineering for Classification
Feature engineering is the task of improving predictive modelling performance on a dataset by transforming its feature space.
Automating Feature Engineering
Feature Engineering is the task of transforming the feature space in a given learning problem to improve the performance of a trained model.
Cognito: Automated Feature Engineering for Supervised Learning
In this paper, we present a novel system called "Cognito", that performs automatic feature engineering on a given dataset for supervised learning.
Feature Selection as a One-Player Game
This paper formalizes Feature Selection as a Reinforcement Learning problem, leading to a provably optimal though intractable selection policy.
