Discovering the Arrow of Time in Machine Learning

Machine learning (ML) is increasingly useful as data grow in volume and accessibility. ML can perform tasks (e.g., categorisation, decision making, anomaly detection, etc.) through experience and without explicit instruction, even when the data are too vast, complex, highly variable, full of errors...

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Autores principales:	J. Kasmire, Anran Zhao
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	machine learning time naive Bayes classification recurrent neural networks Twitter social media data Information technology T58.5-58.64
Acceso en línea:	https://doaj.org/article/3449420e3438493b8a8ffcf4e0d1224d
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id	oai:doaj.org-article:3449420e3438493b8a8ffcf4e0d1224d
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spelling	oai:doaj.org-article:3449420e3438493b8a8ffcf4e0d1224d2021-11-25T17:58:24ZDiscovering the Arrow of Time in Machine Learning10.3390/info121104392078-2489https://doaj.org/article/3449420e3438493b8a8ffcf4e0d1224d2021-10-01T00:00:00Zhttps://www.mdpi.com/2078-2489/12/11/439https://doaj.org/toc/2078-2489Machine learning (ML) is increasingly useful as data grow in volume and accessibility. ML can perform tasks (e.g., categorisation, decision making, anomaly detection, etc.) through experience and without explicit instruction, even when the data are too vast, complex, highly variable, full of errors to be analysed in other ways. Thus, ML is great for natural language, images, or other complex and messy data available in large and growing volumes. Selecting ML models for tasks depends on many factors as they vary in supervision needed, tolerable error levels, and ability to account for order or temporal context, among many other things. Importantly, ML methods for tasks that use explicitly ordered or time-dependent data struggle with errors or data asymmetry. Most data are (implicitly) ordered or time-dependent, potentially allowing a hidden ‘arrow of time’ to affect ML performance on non-temporal tasks. This research explores the interaction of ML and implicit order using two ML models to automatically classify (a non-temporal task) tweets (temporal data) under conditions that balance volume and complexity of data. Results show that performance was affected, suggesting that researchers should carefully consider time when matching appropriate ML models to tasks, even when time is only implicitly included.J. KasmireAnran ZhaoMDPI AGarticlemachine learningtimenaive Bayes classificationrecurrent neural networksTwittersocial media dataInformation technologyT58.5-58.64ENInformation, Vol 12, Iss 439, p 439 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	machine learning time naive Bayes classification recurrent neural networks Twitter social media data Information technology T58.5-58.64
spellingShingle	machine learning time naive Bayes classification recurrent neural networks Twitter social media data Information technology T58.5-58.64 J. Kasmire Anran Zhao Discovering the Arrow of Time in Machine Learning
description	Machine learning (ML) is increasingly useful as data grow in volume and accessibility. ML can perform tasks (e.g., categorisation, decision making, anomaly detection, etc.) through experience and without explicit instruction, even when the data are too vast, complex, highly variable, full of errors to be analysed in other ways. Thus, ML is great for natural language, images, or other complex and messy data available in large and growing volumes. Selecting ML models for tasks depends on many factors as they vary in supervision needed, tolerable error levels, and ability to account for order or temporal context, among many other things. Importantly, ML methods for tasks that use explicitly ordered or time-dependent data struggle with errors or data asymmetry. Most data are (implicitly) ordered or time-dependent, potentially allowing a hidden ‘arrow of time’ to affect ML performance on non-temporal tasks. This research explores the interaction of ML and implicit order using two ML models to automatically classify (a non-temporal task) tweets (temporal data) under conditions that balance volume and complexity of data. Results show that performance was affected, suggesting that researchers should carefully consider time when matching appropriate ML models to tasks, even when time is only implicitly included.
format	article
author	J. Kasmire Anran Zhao
author_facet	J. Kasmire Anran Zhao
author_sort	J. Kasmire
title	Discovering the Arrow of Time in Machine Learning
title_short	Discovering the Arrow of Time in Machine Learning
title_full	Discovering the Arrow of Time in Machine Learning
title_fullStr	Discovering the Arrow of Time in Machine Learning
title_full_unstemmed	Discovering the Arrow of Time in Machine Learning
title_sort	discovering the arrow of time in machine learning
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/3449420e3438493b8a8ffcf4e0d1224d
work_keys_str_mv	AT jkasmire discoveringthearrowoftimeinmachinelearning AT anranzhao discoveringthearrowoftimeinmachinelearning
_version_	1718411793756323840

Discovering the Arrow of Time in Machine Learning

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