Techniques to reject atypical patterns

Lopes, Júlio Castro; Rodrigues, Pedro João

Publication

Techniques to reject atypical patterns

2022Conference paper

dc.contributor.author	Lopes, Júlio Castro
dc.contributor.author	Rodrigues, Pedro João
dc.date.accessioned	2023-03-10T14:48:57Z
dc.date.available	2023-03-10T14:48:57Z
dc.date.issued	2022
dc.description.abstract	Supervised Classification algorithms are only trained to recognize and classify certain patterns, those contained in the training group. Therefore, these will by default, classify the unknown patterns incorrectly, causing unwanted results. This work proposes several solutions, to make the referred algorithms capable of detecting unknown patterns. The main approach for the development of models capable of recognizing these patterns, was the use of three different models of Autoencoders: Simple Autoencoder (SAE), Convolutional Autoencoder (CAE) and Variational Autoencoder (VAE), that are a specific type of Neural Networks. After carrying out several tests on each of the three models of Autoencoders, it was possible to determine which one performed best the task of detecting/rejecting atypical patterns. Afterwards, the performance of the best Autoencoder was compared to the performance of a Convolutional Neural Network (CNN) in the execution of the referred task. The conclusion was that the VAE effectively detected atypical patterns better than the CNN. Some conventional Machine Learning techniques (Support Vector Machine (SVM), Random Forest (RF), Logistic Regression (LR)) were also tested. The one that presented the best performance was the RF classifier, achieving an accuracy of 75% in the detection of atypical/typical patterns. Thus, regarding the classification balance between atypical and typical patterns, Machine Learning techniques were not enough to surpass the Deep Learning methods, where the best accuracy reached 88% for the VAE.	pt_PT
dc.description.version	info:eu-repo/semantics/publishedVersion	pt_PT
dc.identifier.citation	Castro Lopes, Júlio; Rodrigues, Pedro João (2022). Techniques to reject atypical patterns. In 2nd International Conference on Optimization, Learning Algorithms and Applications, OL2A 2022. Bragança. 1754, p. 3-18	pt_PT
dc.identifier.doi	https://doi.org/10.1007/978-3-031-23236-7_1	pt_PT
dc.identifier.isbn	978-3-031-23236-7
dc.identifier.uri	http://hdl.handle.net/10198/27622
dc.language.iso	eng	pt_PT
dc.peerreviewed	yes	pt_PT
dc.publisher	Springer	pt_PT
dc.rights.uri	http://creativecommons.org/licenses/by/4.0/	pt_PT
dc.subject	Deep learning	pt_PT
dc.title	Techniques to reject atypical patterns	pt_PT
dc.type	conference paper
dspace.entity.type	Publication
oaire.citation.conferencePlace	Bragança	pt_PT
oaire.citation.endPage	18	pt_PT
oaire.citation.startPage	3	pt_PT
oaire.citation.title	International Conference on Optimization, Learning Algorithms and Applications - OL2A 2022	pt_PT
oaire.citation.volume	1754	pt_PT
person.familyName	Rodrigues
person.givenName	Pedro João
person.identifier.ciencia-id	1316-21BB-9015
person.identifier.orcid	0000-0002-0555-2029
rcaap.rights	restrictedAccess	pt_PT
rcaap.type	conferenceObject	pt_PT
relation.isAuthorOfPublication	6c5911a6-b62b-4876-9def-60096b52383a
relation.isAuthorOfPublication.latestForDiscovery	6c5911a6-b62b-4876-9def-60096b52383a