{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T16:05:30Z","timestamp":1753891530591,"version":"3.41.2"},"reference-count":27,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2023,11,21]],"date-time":"2023-11-21T00:00:00Z","timestamp":1700524800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Neurorobot."],"abstract":"<jats:p>This study introduces an intelligent learning model for classification tasks, termed the voting-based Double Pseudo-inverse Extreme Learning Machine (V-DPELM) model. Because the traditional method is affected by the weight of input layer and the bias of hidden layer, the number of hidden layer neurons is too large and the model performance is unstable. The V-DPELM model proposed in this paper can greatly alleviate the limitations of traditional models because of its direct determination of weight structure and voting mechanism strategy. Through extensive simulations on various real-world classification datasets, we observe a marked improvement in classification accuracy when comparing the V-DPELM algorithm to traditional V-ELM methods. Notably, when used for machine recognition classification of breast tumors, the V-DPELM method demonstrates superior classification accuracy, positioning it as a valuable tool in machine-assisted breast tumor diagnosis models.<\/jats:p>","DOI":"10.3389\/fnbot.2023.1322645","type":"journal-article","created":{"date-parts":[[2023,11,23]],"date-time":"2023-11-23T15:14:36Z","timestamp":1700752476000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Voting based double-weighted deterministic extreme learning machine model and its application"],"prefix":"10.3389","volume":"17","author":[{"given":"Rongbo","family":"Lu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liang","family":"Luo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bolin","family":"Liao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2023,11,21]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"2390","DOI":"10.3390\/s20082390","article-title":"Review of microwaves techniques for breast cancer detection","volume":"20","author":"Aldhaeebi","year":"2020","journal-title":"Sensors"},{"key":"B2","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1016\/j.ins.2011.09.015","article-title":"Voting based extreme learning machine","volume":"185","author":"Cao","year":"2012","journal-title":"Inf. Sci"},{"key":"B3","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1007\/978-3-030-20301-6_4","article-title":"Breast cancer: current perspectives on the disease status","volume":"1152","author":"Fahad Ullah","year":"2019","journal-title":"Adv. Exp. Med. Biol"},{"key":"B4","doi-asserted-by":"publisher","first-page":"532","DOI":"10.1007\/s10489-018-1282-3","article-title":"A meta extreme learning machine method for forecasting financial time series","volume":"49","author":"Fern\u00e1ndez","year":"2019","journal-title":"Appl. Intell"},{"key":"B5","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1016\/j.neucom.2013.05.047","article-title":"Investigating the use of alternative topologies on performance of the pso-elm","volume":"127","author":"Figueiredo","year":"2014","journal-title":"Neurocomputing"},{"key":"B6","doi-asserted-by":"publisher","first-page":"040506","DOI":"10.7498\/aps.61.040506","article-title":"Prediction of multivariable chaotic time series using optimized extreme learning machine","volume":"61","author":"Gao","year":"2012","journal-title":"Acta Phys. Sin"},{"key":"B7","doi-asserted-by":"publisher","DOI":"10.21437\/Interspeech.2014-57","article-title":"\u201cSpeech emotion recognition using deep neural network and extreme learning machine","author":"Han","year":"2014"},{"volume-title":"Neural Networks: A Comprehensive Foundation","year":"1998","author":"Haykin","key":"B8"},{"key":"B9","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1109\/MCI.2015.2405316","article-title":"Local receptive fields based extreme learning machine","volume":"10","author":"Huang","year":"2015","journal-title":"IEEE Comput. Intell. Magaz"},{"key":"B10","first-page":"985","article-title":"\u201cExtreme learning machine: a new learning scheme of feedforward neural networks","author":"Huang","year":"2004"},{"key":"B11","doi-asserted-by":"publisher","first-page":"489","DOI":"10.1016\/j.neucom.2005.12.126","article-title":"Extreme learning machine: theory and applications","volume":"70","author":"Huang","year":"2006","journal-title":"Neurocomputing"},{"key":"B12","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1016\/j.neucom.2012.11.035","article-title":"A multi-objective micro genetic elm algorithm","volume":"111","author":"Lahoz","year":"2013","journal-title":"Neurocomputing"},{"key":"B13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11912-019-0787-1","article-title":"The impact of obesity on breast cancer diagnosis and treatment","volume":"21","author":"Lee","year":"2019","journal-title":"Curr. Oncol. Rep"},{"key":"B14","doi-asserted-by":"publisher","first-page":"164","DOI":"10.1090\/qam\/10666","article-title":"A method for the solution of certain non-linear problems in least squares","volume":"2","author":"Levenberg","year":"1944","journal-title":"Quart. Appl. Mathem"},{"key":"B15","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1016\/j.neucom.2013.02.052","article-title":"Dissimilarity based ensemble of extreme learning machine for gene expression data classification","volume":"128","author":"Lu","year":"2014","journal-title":"Neurocomputing"},{"key":"B16","doi-asserted-by":"publisher","first-page":"431","DOI":"10.1137\/0111030","article-title":"An algorithm for least-squares estimation of nonlinear parameters","volume":"11","author":"Marquardt","year":"1963","journal-title":"J. Soc. Ind. Appl. Mathem"},{"key":"B17","doi-asserted-by":"publisher","first-page":"158","DOI":"10.1109\/TNN.2009.2036259","article-title":"OP-ELM: optimally pruned extreme learning machine","volume":"21","author":"Miche","year":"2009","journal-title":"IEEE Trans. Neural Netw"},{"key":"B18","doi-asserted-by":"publisher","first-page":"2413","DOI":"10.1016\/j.neucom.2010.12.042","article-title":"Trop-elm: a double-regularized elm using lars and tikhonov regularization","volume":"74","author":"Miche","year":"2011","journal-title":"Neurocomputing"},{"key":"B19","doi-asserted-by":"publisher","first-page":"339","DOI":"10.1109\/TCYB.2016.2514537","article-title":"An incremental type-2 meta-cognitive extreme learning machine","volume":"47","author":"Pratama","year":"2016","journal-title":"IEEE Trans. Cybern"},{"key":"B20","doi-asserted-by":"publisher","first-page":"809","DOI":"10.1109\/TNNLS.2015.2424995","article-title":"Extreme learning machine for multilayer perceptron","volume":"27","author":"Tang","year":"2015","journal-title":"IEEE Trans. Neural Netw. Learn. Syst"},{"key":"B21","doi-asserted-by":"publisher","first-page":"105146","DOI":"10.1109\/ACCESS.2019.2892795","article-title":"Breast cancer detection using extreme learning machine based on feature fusion with cnn deep features","volume":"7","author":"Wang","year":"2019","journal-title":"IEEE Access"},{"key":"B22","doi-asserted-by":"publisher","first-page":"227","DOI":"10.1007\/s00521-014-1764-0","article-title":"Breast tumor detection in double views mammography based on extreme learning machine","volume":"27","author":"Wang","year":"2016","journal-title":"Neural Comput. Applic"},{"key":"B23","first-page":"1656","article-title":"Multiple hidden layer output matrices extreme learning machine","volume":"36","author":"Zhang","year":"2014","journal-title":"Syst. Eng. Electr"},{"key":"B24","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1016\/j.patcog.2016.04.003","article-title":"Memetic extreme learning machine","volume":"58","author":"Zhang","year":"2016","journal-title":"Patt. Recogn"},{"key":"B25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.neucom.2017.01.049","article-title":"Gram-schmidt process based incremental extreme learning machine","volume":"241","author":"Zhao","year":"2017","journal-title":"Neurocomputing"},{"key":"B26","first-page":"2145","article-title":"Application of elm in computer-aided diagnosis of breast tumors based on improved fish swarm optimization algorithm","volume":"39","author":"Zhou","year":"2017","journal-title":"Comput. Eng. Sci"},{"key":"B27","doi-asserted-by":"publisher","first-page":"1759","DOI":"10.1016\/j.patcog.2005.03.028","article-title":"Evolutionary extreme learning machine","volume":"38","author":"Zhu","year":"2005","journal-title":"Patt. Recogn"}],"container-title":["Frontiers in Neurorobotics"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fnbot.2023.1322645\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,23]],"date-time":"2023-11-23T15:14:41Z","timestamp":1700752481000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fnbot.2023.1322645\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,21]]},"references-count":27,"alternative-id":["10.3389\/fnbot.2023.1322645"],"URL":"https:\/\/doi.org\/10.3389\/fnbot.2023.1322645","relation":{},"ISSN":["1662-5218"],"issn-type":[{"type":"electronic","value":"1662-5218"}],"subject":[],"published":{"date-parts":[[2023,11,21]]},"article-number":"1322645"}}