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.
journal_name
Sci Repjournal_title
Scientific reportsauthors
Taniguchi H,Sato H,Shirakawa Tdoi
10.1038/s41598-018-25679-zsubject
Has Abstractpub_date
2018-05-09 00:00:00pages
7397issue
1issn
2045-2322pii
10.1038/s41598-018-25679-zjournal_volume
8pub_type
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