A machine learning model with human cognitive biases capable of learning from small and biased datasets

Abstract Human learners can generalize a new concept from a small number of samples. In contrast, conventional machine learning methods require large amounts of data to address the same types of problems. Humans have cognitive biases that promote fast learning. Here, we developed a method to reduce...

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Autores principales:	Hidetaka Taniguchi, Hiroshi Sato, Tomohiro Shirakawa
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2018
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/ce9a99c739bf4f4896c11549c24389ad
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spelling	oai:doaj.org-article:ce9a99c739bf4f4896c11549c24389ad2021-12-02T12:33:00ZA machine learning model with human cognitive biases capable of learning from small and biased datasets10.1038/s41598-018-25679-z2045-2322https://doaj.org/article/ce9a99c739bf4f4896c11549c24389ad2018-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-018-25679-zhttps://doaj.org/toc/2045-2322Abstract Human learners can generalize a new concept from a small number of samples. In contrast, conventional machine learning methods require large amounts of data to address the same types of problems. Humans have cognitive biases that promote fast learning. Here, we developed a method to reduce the gap between human beings and machines in this type of inference by utilizing cognitive biases. We implemented a human cognitive model into machine learning algorithms and compared their performance with the currently most popular methods, naïve Bayes, support vector machine, neural networks, logistic regression and random forests. We focused on the task of spam classification, which has been studied for a long time in the field of machine learning and often requires a large amount of data to obtain high accuracy. Our models achieved superior performance with small and biased samples in comparison with other representative machine learning methods.Hidetaka TaniguchiHiroshi SatoTomohiro ShirakawaNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 8, Iss 1, Pp 1-13 (2018)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Hidetaka Taniguchi Hiroshi Sato Tomohiro Shirakawa A machine learning model with human cognitive biases capable of learning from small and biased datasets
description	Abstract Human learners can generalize a new concept from a small number of samples. In contrast, conventional machine learning methods require large amounts of data to address the same types of problems. Humans have cognitive biases that promote fast learning. Here, we developed a method to reduce the gap between human beings and machines in this type of inference by utilizing cognitive biases. We implemented a human cognitive model into machine learning algorithms and compared their performance with the currently most popular methods, naïve Bayes, support vector machine, neural networks, logistic regression and random forests. We focused on the task of spam classification, which has been studied for a long time in the field of machine learning and often requires a large amount of data to obtain high accuracy. Our models achieved superior performance with small and biased samples in comparison with other representative machine learning methods.
format	article
author	Hidetaka Taniguchi Hiroshi Sato Tomohiro Shirakawa
author_facet	Hidetaka Taniguchi Hiroshi Sato Tomohiro Shirakawa
author_sort	Hidetaka Taniguchi
title	A machine learning model with human cognitive biases capable of learning from small and biased datasets
title_short	A machine learning model with human cognitive biases capable of learning from small and biased datasets
title_full	A machine learning model with human cognitive biases capable of learning from small and biased datasets
title_fullStr	A machine learning model with human cognitive biases capable of learning from small and biased datasets
title_full_unstemmed	A machine learning model with human cognitive biases capable of learning from small and biased datasets
title_sort	machine learning model with human cognitive biases capable of learning from small and biased datasets
publisher	Nature Portfolio
publishDate	2018
url	https://doaj.org/article/ce9a99c739bf4f4896c11549c24389ad
work_keys_str_mv	AT hidetakataniguchi amachinelearningmodelwithhumancognitivebiasescapableoflearningfromsmallandbiaseddatasets AT hiroshisato amachinelearningmodelwithhumancognitivebiasescapableoflearningfromsmallandbiaseddatasets AT tomohiroshirakawa amachinelearningmodelwithhumancognitivebiasescapableoflearningfromsmallandbiaseddatasets AT hidetakataniguchi machinelearningmodelwithhumancognitivebiasescapableoflearningfromsmallandbiaseddatasets AT hiroshisato machinelearningmodelwithhumancognitivebiasescapableoflearningfromsmallandbiaseddatasets AT tomohiroshirakawa machinelearningmodelwithhumancognitivebiasescapableoflearningfromsmallandbiaseddatasets
_version_	1718393885930029056

A machine learning model with human cognitive biases capable of learning from small and biased datasets

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