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http://hdl.handle.net/1942/36611
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DC Field | Value | Language |
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dc.contributor.author | Jastrzebska, Agnieszka | - |
dc.contributor.author | NAPOLES RUIZ, Gonzalo | - |
dc.contributor.author | Homenda, Wladyslaw | - |
dc.contributor.author | VANHOOF, Koen | - |
dc.date.accessioned | 2022-02-07T09:53:08Z | - |
dc.date.available | 2022-02-07T09:53:08Z | - |
dc.date.issued | 2023 | - |
dc.date.submitted | 2022-02-03T19:50:41Z | - |
dc.identifier.citation | IEEE Transactions on Cybernetics, 53 (2) , p. 1348 - 1359 | - |
dc.identifier.uri | http://hdl.handle.net/1942/36611 | - |
dc.description.abstract | This article presents a comprehensive approach for time-series classification. The proposed model employs a fuzzy cognitive map (FCM) as a classification engine. Preprocessed input data feed the employed FCM. Map responses, after a postprocessing procedure, are used in the calculation of the final classification decision. The time-series data are staged using the moving-window technique to capture the time flow in the training procedure. We use a backward error propagation algorithm to compute the required model hyperparameters. Four model hyperparameters require tuning. Two are crucial for the model construction: 1) FCM size (number of concepts) and 2) window size (for the moving-window technique). Other two are important for training the model: 1) the number of epochs and 2) the learning rate (for training). Two distinguishing aspects of the proposed model are worth noting: 1) the separation of the classification engine from pre- and post-processing and 2) the time flow capture for data from concept space. The proposed classifier joins the key advantage of the FCM model, which is the interpretability of the model, with the superior classification performance attributed to the specially designed pre- and postprocessing stages. This article presents the experiments performed, demonstrating that the proposed model performs well against a wide range of state-of-the-art time-series classification algorithms. | - |
dc.description.sponsorship | POB Research Centre Cybersecurity and Data Science ofWarsaw University | - |
dc.language.iso | en | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.rights | 2021 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See https://www.ieee.org/publications/rights/index.html for more information. | - |
dc.subject.other | Time series analysis | - |
dc.subject.other | Feature extraction | - |
dc.subject.other | Computational modeling | - |
dc.subject.other | Task analysis | - |
dc.subject.other | Hidden Markov models | - |
dc.subject.other | Training | - |
dc.subject.other | Support vector machines | - |
dc.subject.other | Backpropagation | - |
dc.subject.other | deep learning | - |
dc.subject.other | fuzzy cognitive maps (FCMs) | - |
dc.subject.other | fuzzy models | - |
dc.subject.other | time-series classification | - |
dc.title | Fuzzy Cognitive Map-Driven Comprehensive Time-Series Classification | - |
dc.type | Journal Contribution | - |
dc.identifier.epage | 1359 | - |
dc.identifier.issue | 2 | - |
dc.identifier.spage | 1348 | - |
dc.identifier.volume | 53 | - |
local.format.pages | 12 | - |
local.bibliographicCitation.jcat | A1 | - |
dc.description.notes | Jastrzebska, A (corresponding author), Warsaw Univ Technol, Fac Math & Informat Sci, PL-00662 Warsaw, Poland. | - |
dc.description.notes | a.jastrzebska@mini.pw.edu.pl | - |
local.publisher.place | 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA | - |
local.type.refereed | Refereed | - |
local.type.specified | Article | - |
dc.identifier.doi | 10.1109/TCYB.2021.3133597 | - |
dc.identifier.pmid | 34936564 | - |
dc.identifier.isi | 000734072100001 | - |
local.provider.type | wosris | - |
local.description.affiliation | [Jastrzebska, Agnieszka; Homenda, Wladyslaw] Warsaw Univ Technol, Fac Math & Informat Sci, PL-00662 Warsaw, Poland. | - |
local.description.affiliation | [Napoles, Gonzalo] Tilburg Univ, Dept Cognit Sci & Artificial Intelligence, NL-5037 AB Tilburg, Netherlands. | - |
local.description.affiliation | [Vanhoof, Koen] Univ Hasselt, Fac Business Econ, B-3500 Hasselt, Belgium. | - |
local.uhasselt.international | yes | - |
item.fullcitation | Jastrzebska, Agnieszka; NAPOLES RUIZ, Gonzalo; Homenda, Wladyslaw & VANHOOF, Koen (2023) Fuzzy Cognitive Map-Driven Comprehensive Time-Series Classification. In: IEEE Transactions on Cybernetics, 53 (2) , p. 1348 - 1359. | - |
item.fulltext | With Fulltext | - |
item.validation | ecoom 2022 | - |
item.contributor | Jastrzebska, Agnieszka | - |
item.contributor | NAPOLES RUIZ, Gonzalo | - |
item.contributor | Homenda, Wladyslaw | - |
item.contributor | VANHOOF, Koen | - |
item.accessRights | Restricted Access | - |
crisitem.journal.issn | 2168-2267 | - |
crisitem.journal.eissn | 2168-2275 | - |
Appears in Collections: | Research publications |
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Fuzzy_Cognitive_Map-Driven_Comprehensive_Time-Series_Classification.pdf Restricted Access | Published version | 1.47 MB | Adobe PDF | View/Open Request a copy |
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