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.
Benchmarks
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Most implemented papers
The autofeat Python Library for Automated Feature Engineering and Selection
This paper describes the autofeat Python library, which provides scikit-learn style linear regression and classification models with automated feature engineering and selection capabilities.
Towards automated feature engineering for credit card fraud detection using multi-perspective HMMs
Our multiple perspectives HMM-based approach offers automated feature engineering to model temporal correlations so as to improve the effectiveness of the classification task and allows for an increase in the detection of fraudulent transactions when combined with the state of the art expert based feature engineering strategy for credit card fraud detection.
Lifting Interpretability-Performance Trade-off via Automated Feature Engineering
Can we train interpretable and accurate models, without timeless feature engineering?
DIFER: Differentiable Automated Feature Engineering
pasalab/difer
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Extensive experiments on classification and regression datasets demonstrate that DIFER can significantly improve the performance of various machine learning algorithms and outperform current state-of-the-art AutoFE methods in terms of both efficiency and performance.
fseval: A Benchmarking Framework for Feature Selection and Feature Ranking Algorithms
The package is open source and can be installed through PyPI.
Feature Programming for Multivariate Time Series Prediction
We introduce the concept of programmable feature engineering for time series modeling and propose a feature programming framework.
Feature Interaction Aware Automated Data Representation Transformation
Creating an effective representation space is crucial for mitigating the curse of dimensionality, enhancing model generalization, addressing data sparsity, and leveraging classical models more effectively.
