Impact of Box-Cox Transformation on Machine-Learning Algorithms
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Author / Producer
Date
2022-04-07
Publication Type
Journal Article
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yes
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Abstract
This paper studied the effects of applying the Box-Cox transformation for classification tasks. Different optimization strategies were evaluated, and the results were promising on four synthetic datasets and two real-world datasets. A consistent improvement in accuracy was demonstrated using a grid exploration with cross-validation. In conclusion, applying the Box-Cox transformation could drastically improve the performance by up to a 12% accuracy increase. Moreover, the Box-Cox parameter choice was dependent on the data and the used classifier.
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Publication status
published
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Book title
Journal / series
Volume
5
Pages / Article No.
877569
Publisher
Frontiers Media
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Edition / version
Methods
Software
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Date collected
Date created
Subject
Box-Cox transformation; power transformation; Non-linear mappings; feature transformation; accuracy improvement; classifier optimization; preprocessing data; monotonic transformation
Organisational unit
09715 - Menon, Carlo / Menon, Carlo