Wavelet Probabilistic Neural Networks
In this article, a novel wavelet probabilistic neural network (WPNN), which is a generative-learning wavelet neural network that relies on the wavelet-based estimation of class probability densities, is proposed. In this new neural network approach, the number of basis functions employed is independ...
Gespeichert in:
| Veröffentlicht in: | IEEE transaction on neural networks and learning systems 2024-01, Vol.35 (1), p.376-389 |
|---|---|
| Hauptverfasser: | , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 389 |
|---|---|
| container_issue | 1 |
| container_start_page | 376 |
| container_title | IEEE transaction on neural networks and learning systems |
| container_volume | 35 |
| creator | Garcia-Trevino, Edgar S. Yang, Pu Barria, Javier A. |
| description | In this article, a novel wavelet probabilistic neural network (WPNN), which is a generative-learning wavelet neural network that relies on the wavelet-based estimation of class probability densities, is proposed. In this new neural network approach, the number of basis functions employed is independent of the number of data inputs, and in that sense, it overcomes the well-known drawback of traditional probabilistic neural networks (PNNs). Since the parameters of the proposed network are updated at a low and constant computational cost, it is particularly aimed at data stream classification and anomaly detection in off-line settings and online environments where the length of data is assumed to be unconstrained. Both synthetic and real-world datasets are used to assess the proposed WPNN. Significant performance enhancements are attained compared to state-of-the-art algorithms. |
| doi_str_mv | 10.1109/TNNLS.2022.3174705 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_RIE</sourceid><recordid>TN_cdi_proquest_journals_2911489178</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>9782514</ieee_id><sourcerecordid>2670063809</sourcerecordid><originalsourceid>FETCH-LOGICAL-c395t-132b2ba895cf7db940ca1b7686cdd19d21542013ed7c971a95553eb8e1e0cb1a3</originalsourceid><addsrcrecordid>eNpdkEtLAzEQgIMoVmr_gIIURPDSmkk2r6MUX1CqYEVvIclOYeu2W5NdxX_v1tYenMsMzDfDzEfICdAhADVX08lk_DxklLEhB5UpKvbIEQPJBoxrvb-r1VuH9FKa0zYkFTIzh6TDhQQFWh6Ri1f3iSXW_adYeeeLskh1EfoTbKIr21R_VfE9HZODmSsT9ra5S15ub6aj-8H48e5hdD0eBG5EPQDOPPNOGxFmKvcmo8GBV1LLkOdgcgYiYxQ45ioYBc4IITh6jYA0eHC8Sy43e1ex-mgw1XZRpIBl6ZZYNckyqdovuKamRc__ofOqicv2OssMQKYNKN1SbEOFWKUUcWZXsVi4-G2B2rVH--vRrj3arcd26Gy7uvELzHcjf9Za4HQDFIi4axulmYCM_wBvxHSO</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2911489178</pqid></control><display><type>article</type><title>Wavelet Probabilistic Neural Networks</title><source>IEEE Electronic Library (IEL)</source><creator>Garcia-Trevino, Edgar S. ; Yang, Pu ; Barria, Javier A.</creator><creatorcontrib>Garcia-Trevino, Edgar S. ; Yang, Pu ; Barria, Javier A.</creatorcontrib><description>In this article, a novel wavelet probabilistic neural network (WPNN), which is a generative-learning wavelet neural network that relies on the wavelet-based estimation of class probability densities, is proposed. In this new neural network approach, the number of basis functions employed is independent of the number of data inputs, and in that sense, it overcomes the well-known drawback of traditional probabilistic neural networks (PNNs). Since the parameters of the proposed network are updated at a low and constant computational cost, it is particularly aimed at data stream classification and anomaly detection in off-line settings and online environments where the length of data is assumed to be unconstrained. Both synthetic and real-world datasets are used to assess the proposed WPNN. Significant performance enhancements are attained compared to state-of-the-art algorithms.</description><identifier>ISSN: 2162-237X</identifier><identifier>EISSN: 2162-2388</identifier><identifier>DOI: 10.1109/TNNLS.2022.3174705</identifier><identifier>PMID: 35617186</identifier><identifier>CODEN: ITNNAL</identifier><language>eng</language><publisher>United States: IEEE</publisher><subject>Algorithms ; Anomalies ; Basis functions ; Biological neural networks ; Data stream classification ; Data transmission ; Estimation ; Neural networks ; Neurons ; nonstationary environment ; online learning ; Probabilistic logic ; probabilistic neural network (PNN) ; Statistical analysis ; Stream classification ; Training ; Training data ; wavelet density estimation ; wavelet frames ; wavelet probabilistic neural networks (WPNNs)</subject><ispartof>IEEE transaction on neural networks and learning systems, 2024-01, Vol.35 (1), p.376-389</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2024</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c395t-132b2ba895cf7db940ca1b7686cdd19d21542013ed7c971a95553eb8e1e0cb1a3</citedby><cites>FETCH-LOGICAL-c395t-132b2ba895cf7db940ca1b7686cdd19d21542013ed7c971a95553eb8e1e0cb1a3</cites><orcidid>0000-0003-4111-439X ; 0000-0001-5935-555X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/9782514$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>315,781,785,797,27926,27927,54760</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/9782514$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/35617186$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Garcia-Trevino, Edgar S.</creatorcontrib><creatorcontrib>Yang, Pu</creatorcontrib><creatorcontrib>Barria, Javier A.</creatorcontrib><title>Wavelet Probabilistic Neural Networks</title><title>IEEE transaction on neural networks and learning systems</title><addtitle>TNNLS</addtitle><addtitle>IEEE Trans Neural Netw Learn Syst</addtitle><description>In this article, a novel wavelet probabilistic neural network (WPNN), which is a generative-learning wavelet neural network that relies on the wavelet-based estimation of class probability densities, is proposed. In this new neural network approach, the number of basis functions employed is independent of the number of data inputs, and in that sense, it overcomes the well-known drawback of traditional probabilistic neural networks (PNNs). Since the parameters of the proposed network are updated at a low and constant computational cost, it is particularly aimed at data stream classification and anomaly detection in off-line settings and online environments where the length of data is assumed to be unconstrained. Both synthetic and real-world datasets are used to assess the proposed WPNN. Significant performance enhancements are attained compared to state-of-the-art algorithms.</description><subject>Algorithms</subject><subject>Anomalies</subject><subject>Basis functions</subject><subject>Biological neural networks</subject><subject>Data stream classification</subject><subject>Data transmission</subject><subject>Estimation</subject><subject>Neural networks</subject><subject>Neurons</subject><subject>nonstationary environment</subject><subject>online learning</subject><subject>Probabilistic logic</subject><subject>probabilistic neural network (PNN)</subject><subject>Statistical analysis</subject><subject>Stream classification</subject><subject>Training</subject><subject>Training data</subject><subject>wavelet density estimation</subject><subject>wavelet frames</subject><subject>wavelet probabilistic neural networks (WPNNs)</subject><issn>2162-237X</issn><issn>2162-2388</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNpdkEtLAzEQgIMoVmr_gIIURPDSmkk2r6MUX1CqYEVvIclOYeu2W5NdxX_v1tYenMsMzDfDzEfICdAhADVX08lk_DxklLEhB5UpKvbIEQPJBoxrvb-r1VuH9FKa0zYkFTIzh6TDhQQFWh6Ri1f3iSXW_adYeeeLskh1EfoTbKIr21R_VfE9HZODmSsT9ra5S15ub6aj-8H48e5hdD0eBG5EPQDOPPNOGxFmKvcmo8GBV1LLkOdgcgYiYxQ45ioYBc4IITh6jYA0eHC8Sy43e1ex-mgw1XZRpIBl6ZZYNckyqdovuKamRc__ofOqicv2OssMQKYNKN1SbEOFWKUUcWZXsVi4-G2B2rVH--vRrj3arcd26Gy7uvELzHcjf9Za4HQDFIi4axulmYCM_wBvxHSO</recordid><startdate>20240101</startdate><enddate>20240101</enddate><creator>Garcia-Trevino, Edgar S.</creator><creator>Yang, Pu</creator><creator>Barria, Javier A.</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QF</scope><scope>7QO</scope><scope>7QP</scope><scope>7QQ</scope><scope>7QR</scope><scope>7SC</scope><scope>7SE</scope><scope>7SP</scope><scope>7SR</scope><scope>7TA</scope><scope>7TB</scope><scope>7TK</scope><scope>7U5</scope><scope>8BQ</scope><scope>8FD</scope><scope>F28</scope><scope>FR3</scope><scope>H8D</scope><scope>JG9</scope><scope>JQ2</scope><scope>KR7</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>P64</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0003-4111-439X</orcidid><orcidid>https://orcid.org/0000-0001-5935-555X</orcidid></search><sort><creationdate>20240101</creationdate><title>Wavelet Probabilistic Neural Networks</title><author>Garcia-Trevino, Edgar S. ; Yang, Pu ; Barria, Javier A.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c395t-132b2ba895cf7db940ca1b7686cdd19d21542013ed7c971a95553eb8e1e0cb1a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Algorithms</topic><topic>Anomalies</topic><topic>Basis functions</topic><topic>Biological neural networks</topic><topic>Data stream classification</topic><topic>Data transmission</topic><topic>Estimation</topic><topic>Neural networks</topic><topic>Neurons</topic><topic>nonstationary environment</topic><topic>online learning</topic><topic>Probabilistic logic</topic><topic>probabilistic neural network (PNN)</topic><topic>Statistical analysis</topic><topic>Stream classification</topic><topic>Training</topic><topic>Training data</topic><topic>wavelet density estimation</topic><topic>wavelet frames</topic><topic>wavelet probabilistic neural networks (WPNNs)</topic><toplevel>online_resources</toplevel><creatorcontrib>Garcia-Trevino, Edgar S.</creatorcontrib><creatorcontrib>Yang, Pu</creatorcontrib><creatorcontrib>Barria, Javier A.</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Aluminium Industry Abstracts</collection><collection>Biotechnology Research Abstracts</collection><collection>Calcium & Calcified Tissue Abstracts</collection><collection>Ceramic Abstracts</collection><collection>Chemoreception Abstracts</collection><collection>Computer and Information Systems Abstracts</collection><collection>Corrosion Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>Materials Business File</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Neurosciences Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>IEEE transaction on neural networks and learning systems</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Garcia-Trevino, Edgar S.</au><au>Yang, Pu</au><au>Barria, Javier A.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Wavelet Probabilistic Neural Networks</atitle><jtitle>IEEE transaction on neural networks and learning systems</jtitle><stitle>TNNLS</stitle><addtitle>IEEE Trans Neural Netw Learn Syst</addtitle><date>2024-01-01</date><risdate>2024</risdate><volume>35</volume><issue>1</issue><spage>376</spage><epage>389</epage><pages>376-389</pages><issn>2162-237X</issn><eissn>2162-2388</eissn><coden>ITNNAL</coden><abstract>In this article, a novel wavelet probabilistic neural network (WPNN), which is a generative-learning wavelet neural network that relies on the wavelet-based estimation of class probability densities, is proposed. In this new neural network approach, the number of basis functions employed is independent of the number of data inputs, and in that sense, it overcomes the well-known drawback of traditional probabilistic neural networks (PNNs). Since the parameters of the proposed network are updated at a low and constant computational cost, it is particularly aimed at data stream classification and anomaly detection in off-line settings and online environments where the length of data is assumed to be unconstrained. Both synthetic and real-world datasets are used to assess the proposed WPNN. Significant performance enhancements are attained compared to state-of-the-art algorithms.</abstract><cop>United States</cop><pub>IEEE</pub><pmid>35617186</pmid><doi>10.1109/TNNLS.2022.3174705</doi><tpages>14</tpages><orcidid>https://orcid.org/0000-0003-4111-439X</orcidid><orcidid>https://orcid.org/0000-0001-5935-555X</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISSN: 2162-237X |
| ispartof | IEEE transaction on neural networks and learning systems, 2024-01, Vol.35 (1), p.376-389 |
| issn | 2162-237X 2162-2388 |
| language | eng |
| recordid | cdi_proquest_journals_2911489178 |
| source | IEEE Electronic Library (IEL) |
| subjects | Algorithms Anomalies Basis functions Biological neural networks Data stream classification Data transmission Estimation Neural networks Neurons nonstationary environment online learning Probabilistic logic probabilistic neural network (PNN) Statistical analysis Stream classification Training Training data wavelet density estimation wavelet frames wavelet probabilistic neural networks (WPNNs) |
| title | Wavelet Probabilistic Neural Networks |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-18T11%3A13%3A55IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_RIE&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Wavelet%20Probabilistic%20Neural%20Networks&rft.jtitle=IEEE%20transaction%20on%20neural%20networks%20and%20learning%20systems&rft.au=Garcia-Trevino,%20Edgar%20S.&rft.date=2024-01-01&rft.volume=35&rft.issue=1&rft.spage=376&rft.epage=389&rft.pages=376-389&rft.issn=2162-237X&rft.eissn=2162-2388&rft.coden=ITNNAL&rft_id=info:doi/10.1109/TNNLS.2022.3174705&rft_dat=%3Cproquest_RIE%3E2670063809%3C/proquest_RIE%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2911489178&rft_id=info:pmid/35617186&rft_ieee_id=9782514&rfr_iscdi=true |