A Study on Different Text Representation Methods for the Negative Selection Algorithm

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dc.contributor.authorFerraria M.A.
dc.contributor.authorFerraria V.A.
dc.contributor.authorde Castro L.N.
dc.date.accessioned2024-03-12T19:12:40Z
dc.date.available2024-03-12T19:12:40Z
dc.date.issued2023
dc.description.abstract© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.Unstructured data, such as text, usually have to be structured before standard machine learning classifiers are applied. In such cases, different representation schemes can be used, such as Bag of Words, the Linguistic Inquiry and Word Count (LIWC), Part-of-Speech Tagging (POS Tagging), and others. The Negative Selection Algorithm (NSA) was designed with inspiration in the immune system to solve binary classification problems, more specifically anomaly detection. This paper investigates the performance of various text representation schemes as input to the NSA. Three different datasets and text representation methods are used, and the results are presented in terms of Accuracy and False Positive Rate.
dc.description.firstpage302
dc.description.lastpage311
dc.description.volume583 LNNS
dc.identifier.doi10.1007/978-3-031-20859-1_30
dc.identifier.issn2367-3389
dc.identifier.urihttps://dspace.mackenzie.br/handle/10899/34241
dc.relation.ispartofLecture Notes in Networks and Systems
dc.rightsAcesso Restrito
dc.subject.otherlanguageBinary classification
dc.subject.otherlanguageBOW
dc.subject.otherlanguageLIWC
dc.subject.otherlanguageNegative selection algorithm
dc.subject.otherlanguagePOS Tagging
dc.subject.otherlanguageText representation
dc.titleA Study on Different Text Representation Methods for the Negative Selection Algorithm
dc.typeArtigo de evento
local.scopus.citations0
local.scopus.eid2-s2.0-85144981932
local.scopus.updated2024-12-01
local.scopus.urlhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85144981932&origin=inward
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