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Special issue on advances in feature engineering editorial

Special issue on advances in feature engineering editorial
Special issue on advances in feature engineering editorial
In order to improve the performance of any machine learning model, it is important to focus more on the data itself instead of continuously developing new algorithms. This is exactly the aim of feature engineering. It can be defined as the clever engineering of data hereby exploiting the intrinsic bias of the machine learning technique to our benefit, ideally both in terms of accuracy and interpretability at the same time. Often times it will be applied in combination with simple machine learning techniques such as regression models or decision trees to boost their performance (whilst maintaining the interpretability property which is so often needed in analytical modeling) but it may also improve complex techniques such as XG Boost and neural networks. Feature engineering aims at designing smart features in one of two possible ways: either by adjusting existing features using various transformations or by extracting or creating new meaningful features (a process often called “featurization”) from different sources (e.g., transactional data, network data, time series data, text data, etc.).
Applied machine learning, Data engineering, Featurization, Interpretability
Verdonck, Tim
60db0690-e4a2-41b2-b1e8-c9e21f0e9ec5
Baesens, Bart
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Óskarsdóttir, María
1622b6dd-5d25-4228-9418-a1729e9577e0
Broucke, Seppe Vanden
0b17d31c-7378-4aa6-a1a8-715ddd08b3b5
Verdonck, Tim
60db0690-e4a2-41b2-b1e8-c9e21f0e9ec5
Baesens, Bart
f7c6496b-aa7f-4026-8616-ca61d9e216f0
Óskarsdóttir, María
1622b6dd-5d25-4228-9418-a1729e9577e0
Broucke, Seppe Vanden
0b17d31c-7378-4aa6-a1a8-715ddd08b3b5

Verdonck, Tim, Baesens, Bart, Óskarsdóttir, María and Broucke, Seppe Vanden (2021) Special issue on advances in feature engineering editorial. Machine Learning. (doi:10.1007/s10994-021-06042-2).

Record type: Editorial

Abstract

In order to improve the performance of any machine learning model, it is important to focus more on the data itself instead of continuously developing new algorithms. This is exactly the aim of feature engineering. It can be defined as the clever engineering of data hereby exploiting the intrinsic bias of the machine learning technique to our benefit, ideally both in terms of accuracy and interpretability at the same time. Often times it will be applied in combination with simple machine learning techniques such as regression models or decision trees to boost their performance (whilst maintaining the interpretability property which is so often needed in analytical modeling) but it may also improve complex techniques such as XG Boost and neural networks. Feature engineering aims at designing smart features in one of two possible ways: either by adjusting existing features using various transformations or by extracting or creating new meaningful features (a process often called “featurization”) from different sources (e.g., transactional data, network data, time series data, text data, etc.).

Text
Editorial_Advances_in_Feature_Engineering - Accepted Manuscript
Restricted to Repository staff only until 6 August 2022.
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More information

Submitted date: 2021
Accepted/In Press date: 6 June 2021
Published date: 6 August 2021
Keywords: Applied machine learning, Data engineering, Featurization, Interpretability

Identifiers

Local EPrints ID: 451870
URI: http://eprints.soton.ac.uk/id/eprint/451870
PURE UUID: 39cc9b82-4a16-42c1-9d49-c72c57affa5e
ORCID for Bart Baesens: ORCID iD orcid.org/0000-0002-5831-5668

Catalogue record

Date deposited: 02 Nov 2021 17:41
Last modified: 28 Apr 2022 01:53

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Contributors

Author: Tim Verdonck
Author: Bart Baesens ORCID iD
Author: María Óskarsdóttir
Author: Seppe Vanden Broucke

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