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
FeatGeNN: Improving Model Performance for Tabular Data with Correlation-based Feature Extraction
Automated Feature Engineering (AutoFE) has become an important task for any machine learning project, as it can help improve model performance and gain more information for statistical analysis.
Toward Efficient Automated Feature Engineering
Specifically, we construct the AFE pipeline based on reinforcement learning setting, where each feature is assigned an agent to perform feature transformation \com{and} selection, and the evaluation score of the produced features in downstream tasks serve as the reward to update the policy.
Feature Selection with Distance Correlation
Choosing which properties of the data to use as input to multivariate decision algorithms -- a. k. a.
Automated Feature Extraction on AsMap for Emotion Classification using EEG
With the advancements in deep learning as a tool for automated feature engineering, in this work, a hybrid of manual and automatic feature extraction methods has been proposed.
Machine Learning for Detecting Data Exfiltration: A Review
Objective: This paper aims at systematically reviewing ML-based data exfiltration countermeasures to identify and classify ML approaches, feature engineering techniques, evaluation datasets, and performance metrics used for these countermeasures.
A Systematic Literature Review on the Use of Deep Learning in Software Engineering Research
An increasingly popular set of techniques adopted by software engineering (SE) researchers to automate development tasks are those rooted in the concept of Deep Learning (DL).
Benchmark Performance of Machine And Deep Learning Based Methodologies for Urdu Text Document Classification
Second, it investigates the performance impact of traditional machine learning based Urdu text document classification methodologies by embedding 10 filter-based feature selection algorithms which have been widely used for other languages.
Statistical and machine learning ensemble modelling to forecast sea surface temperature
Training data consisted of satellite-derived SST and atmospheric data from The Weather Company.
Techniques for Automated Machine Learning
Automated machine learning (AutoML) aims to find optimal machine learning solutions automatically given a machine learning problem.
Exploiting Unsupervised Pre-training and Automated Feature Engineering for Low-resource Hate Speech Detection in Polish
This paper presents our contribution to PolEval 2019 Task 6: Hate speech and bullying detection.
