Loading…
Surrogate-Assisted Symbolic Time-Series Discretization Using Multi-Breakpoints and a Multi-Objective Evolutionary Algorithm
The enhanced multi-objective symbolic discretization for time series (eMODiTS) method employs a flexible discretization scheme using different value cuts for each non-equal time interval, which incurs a high computational cost for evaluating each objective function. It is essential to mention that e...
Saved in:
| Published in: | Mathematical and computational applications 2024-09, Vol.29 (5), p.78 |
|---|---|
| Main Authors: | , , , |
| Format: | Article |
| Language: | English |
| Citations: | Items that this one cites |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| cited_by | |
|---|---|
| cites | cdi_FETCH-LOGICAL-c738-5972f07aa5ac8b4060ea11a62be1fb089da265681c78ad4ed1a5808de29127da3 |
| container_end_page | |
| container_issue | 5 |
| container_start_page | 78 |
| container_title | Mathematical and computational applications |
| container_volume | 29 |
| creator | Márquez-Grajales, Aldo Mezura-Montes, Efrén Acosta-Mesa, Héctor-Gabriel Salas-Martínez, Fernando |
| description | The enhanced multi-objective symbolic discretization for time series (eMODiTS) method employs a flexible discretization scheme using different value cuts for each non-equal time interval, which incurs a high computational cost for evaluating each objective function. It is essential to mention that each solution found by eMODiTS is a different-sized vector. Previous work was performed where surrogate models were implemented to reduce the computational cost to solve this problem. However, low-fidelity approximations were obtained concerning the original model. Consequently, our main objective is to propose an improvement to this work, modifying the updating process of the surrogate models to minimize their disadvantages. This improvement was evaluated based on classification, predictive power, and computational cost, comparing it against the original model and ten discretization methods reported in the literature. The results suggest that the proposal achieves a higher fidelity to the original model than previous work. It also achieved a computational cost reduction rate between 15% and 80% concerning the original model. Finally, the classification error of our proposal is similar to eMODiTS and maintains its behavior compared to the other discretization methods. |
| doi_str_mv | 10.3390/mca29050078 |
| format | article |
| fullrecord | <record><control><sourceid>crossref</sourceid><recordid>TN_cdi_crossref_primary_10_3390_mca29050078</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>10_3390_mca29050078</sourcerecordid><originalsourceid>FETCH-LOGICAL-c738-5972f07aa5ac8b4060ea11a62be1fb089da265681c78ad4ed1a5808de29127da3</originalsourceid><addsrcrecordid>eNpNkEtLAzEUhYMoWGpX_oHsZfQm80hmWWt9QKWL1vVwJ3Onps6jJJlC9c9rsYuuzuHAORw-xm4F3MdxDg-tQZlDCqD0BRtJmatIq0RdnvlrNvF-CwBSJCABRuxnNTjXbzBQNPXe-kAVXx3asm-s4WvbUrQiZ8nzJ-uNo2C_Mdi-4x_edhv-PjTBRo-O8GvX2y54jl3F8ZQvyy2ZYPfE5_u-GY49dAc-bTa9s-GzvWFXNTaeJicds_XzfD17jRbLl7fZdBEZFesozZWsQSGmaHSZQAaEQmAmSxJ1CTqvUGZppoVRGquEKoGpBl2RzIVUFcZjdvc_a1zvvaO62Dnb_j0pBBRHcsUZufgXTP1j1Q</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>Surrogate-Assisted Symbolic Time-Series Discretization Using Multi-Breakpoints and a Multi-Objective Evolutionary Algorithm</title><source>Publicly Available Content Database</source><source>EZB Electronic Journals Library</source><creator>Márquez-Grajales, Aldo ; Mezura-Montes, Efrén ; Acosta-Mesa, Héctor-Gabriel ; Salas-Martínez, Fernando</creator><creatorcontrib>Márquez-Grajales, Aldo ; Mezura-Montes, Efrén ; Acosta-Mesa, Héctor-Gabriel ; Salas-Martínez, Fernando</creatorcontrib><description>The enhanced multi-objective symbolic discretization for time series (eMODiTS) method employs a flexible discretization scheme using different value cuts for each non-equal time interval, which incurs a high computational cost for evaluating each objective function. It is essential to mention that each solution found by eMODiTS is a different-sized vector. Previous work was performed where surrogate models were implemented to reduce the computational cost to solve this problem. However, low-fidelity approximations were obtained concerning the original model. Consequently, our main objective is to propose an improvement to this work, modifying the updating process of the surrogate models to minimize their disadvantages. This improvement was evaluated based on classification, predictive power, and computational cost, comparing it against the original model and ten discretization methods reported in the literature. The results suggest that the proposal achieves a higher fidelity to the original model than previous work. It also achieved a computational cost reduction rate between 15% and 80% concerning the original model. Finally, the classification error of our proposal is similar to eMODiTS and maintains its behavior compared to the other discretization methods.</description><identifier>ISSN: 2297-8747</identifier><identifier>EISSN: 2297-8747</identifier><identifier>DOI: 10.3390/mca29050078</identifier><language>eng</language><ispartof>Mathematical and computational applications, 2024-09, Vol.29 (5), p.78</ispartof><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c738-5972f07aa5ac8b4060ea11a62be1fb089da265681c78ad4ed1a5808de29127da3</cites><orcidid>0000-0002-5104-5167 ; 0000-0002-1565-5267 ; 0000-0002-0935-7642 ; 0000-0001-9567-2069</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>315,786,790,27957,27958</link.rule.ids></links><search><creatorcontrib>Márquez-Grajales, Aldo</creatorcontrib><creatorcontrib>Mezura-Montes, Efrén</creatorcontrib><creatorcontrib>Acosta-Mesa, Héctor-Gabriel</creatorcontrib><creatorcontrib>Salas-Martínez, Fernando</creatorcontrib><title>Surrogate-Assisted Symbolic Time-Series Discretization Using Multi-Breakpoints and a Multi-Objective Evolutionary Algorithm</title><title>Mathematical and computational applications</title><description>The enhanced multi-objective symbolic discretization for time series (eMODiTS) method employs a flexible discretization scheme using different value cuts for each non-equal time interval, which incurs a high computational cost for evaluating each objective function. It is essential to mention that each solution found by eMODiTS is a different-sized vector. Previous work was performed where surrogate models were implemented to reduce the computational cost to solve this problem. However, low-fidelity approximations were obtained concerning the original model. Consequently, our main objective is to propose an improvement to this work, modifying the updating process of the surrogate models to minimize their disadvantages. This improvement was evaluated based on classification, predictive power, and computational cost, comparing it against the original model and ten discretization methods reported in the literature. The results suggest that the proposal achieves a higher fidelity to the original model than previous work. It also achieved a computational cost reduction rate between 15% and 80% concerning the original model. Finally, the classification error of our proposal is similar to eMODiTS and maintains its behavior compared to the other discretization methods.</description><issn>2297-8747</issn><issn>2297-8747</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><recordid>eNpNkEtLAzEUhYMoWGpX_oHsZfQm80hmWWt9QKWL1vVwJ3Onps6jJJlC9c9rsYuuzuHAORw-xm4F3MdxDg-tQZlDCqD0BRtJmatIq0RdnvlrNvF-CwBSJCABRuxnNTjXbzBQNPXe-kAVXx3asm-s4WvbUrQiZ8nzJ-uNo2C_Mdi-4x_edhv-PjTBRo-O8GvX2y54jl3F8ZQvyy2ZYPfE5_u-GY49dAc-bTa9s-GzvWFXNTaeJicds_XzfD17jRbLl7fZdBEZFesozZWsQSGmaHSZQAaEQmAmSxJ1CTqvUGZppoVRGquEKoGpBl2RzIVUFcZjdvc_a1zvvaO62Dnb_j0pBBRHcsUZufgXTP1j1Q</recordid><startdate>20240911</startdate><enddate>20240911</enddate><creator>Márquez-Grajales, Aldo</creator><creator>Mezura-Montes, Efrén</creator><creator>Acosta-Mesa, Héctor-Gabriel</creator><creator>Salas-Martínez, Fernando</creator><scope>AAYXX</scope><scope>CITATION</scope><orcidid>https://orcid.org/0000-0002-5104-5167</orcidid><orcidid>https://orcid.org/0000-0002-1565-5267</orcidid><orcidid>https://orcid.org/0000-0002-0935-7642</orcidid><orcidid>https://orcid.org/0000-0001-9567-2069</orcidid></search><sort><creationdate>20240911</creationdate><title>Surrogate-Assisted Symbolic Time-Series Discretization Using Multi-Breakpoints and a Multi-Objective Evolutionary Algorithm</title><author>Márquez-Grajales, Aldo ; Mezura-Montes, Efrén ; Acosta-Mesa, Héctor-Gabriel ; Salas-Martínez, Fernando</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c738-5972f07aa5ac8b4060ea11a62be1fb089da265681c78ad4ed1a5808de29127da3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Márquez-Grajales, Aldo</creatorcontrib><creatorcontrib>Mezura-Montes, Efrén</creatorcontrib><creatorcontrib>Acosta-Mesa, Héctor-Gabriel</creatorcontrib><creatorcontrib>Salas-Martínez, Fernando</creatorcontrib><collection>CrossRef</collection><jtitle>Mathematical and computational applications</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Márquez-Grajales, Aldo</au><au>Mezura-Montes, Efrén</au><au>Acosta-Mesa, Héctor-Gabriel</au><au>Salas-Martínez, Fernando</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Surrogate-Assisted Symbolic Time-Series Discretization Using Multi-Breakpoints and a Multi-Objective Evolutionary Algorithm</atitle><jtitle>Mathematical and computational applications</jtitle><date>2024-09-11</date><risdate>2024</risdate><volume>29</volume><issue>5</issue><spage>78</spage><pages>78-</pages><issn>2297-8747</issn><eissn>2297-8747</eissn><abstract>The enhanced multi-objective symbolic discretization for time series (eMODiTS) method employs a flexible discretization scheme using different value cuts for each non-equal time interval, which incurs a high computational cost for evaluating each objective function. It is essential to mention that each solution found by eMODiTS is a different-sized vector. Previous work was performed where surrogate models were implemented to reduce the computational cost to solve this problem. However, low-fidelity approximations were obtained concerning the original model. Consequently, our main objective is to propose an improvement to this work, modifying the updating process of the surrogate models to minimize their disadvantages. This improvement was evaluated based on classification, predictive power, and computational cost, comparing it against the original model and ten discretization methods reported in the literature. The results suggest that the proposal achieves a higher fidelity to the original model than previous work. It also achieved a computational cost reduction rate between 15% and 80% concerning the original model. Finally, the classification error of our proposal is similar to eMODiTS and maintains its behavior compared to the other discretization methods.</abstract><doi>10.3390/mca29050078</doi><orcidid>https://orcid.org/0000-0002-5104-5167</orcidid><orcidid>https://orcid.org/0000-0002-1565-5267</orcidid><orcidid>https://orcid.org/0000-0002-0935-7642</orcidid><orcidid>https://orcid.org/0000-0001-9567-2069</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 2297-8747 |
| ispartof | Mathematical and computational applications, 2024-09, Vol.29 (5), p.78 |
| issn | 2297-8747 2297-8747 |
| language | eng |
| recordid | cdi_crossref_primary_10_3390_mca29050078 |
| source | Publicly Available Content Database; EZB Electronic Journals Library |
| title | Surrogate-Assisted Symbolic Time-Series Discretization Using Multi-Breakpoints and a Multi-Objective Evolutionary Algorithm |
| url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-09-22T22%3A26%3A31IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-crossref&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Surrogate-Assisted%20Symbolic%20Time-Series%20Discretization%20Using%20Multi-Breakpoints%20and%20a%20Multi-Objective%20Evolutionary%20Algorithm&rft.jtitle=Mathematical%20and%20computational%20applications&rft.au=M%C3%A1rquez-Grajales,%20Aldo&rft.date=2024-09-11&rft.volume=29&rft.issue=5&rft.spage=78&rft.pages=78-&rft.issn=2297-8747&rft.eissn=2297-8747&rft_id=info:doi/10.3390/mca29050078&rft_dat=%3Ccrossref%3E10_3390_mca29050078%3C/crossref%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c738-5972f07aa5ac8b4060ea11a62be1fb089da265681c78ad4ed1a5808de29127da3%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true |