A modified forward‐backward splitting methods for the sum of two monotone operators with applications to breast cancer prediction
This work proposes a modified forward‐backward splitting algorithm combining an inertial technique for solving the monotone variational inclusion problem. The weak convergence theorem is established under some suitable conditions in Hilbert space, and a new step size is presented for our algorithm t...
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| Vydané v: | Mathematical methods in the applied sciences Ročník 46; číslo 1; s. 1251 - 1265 |
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| Hlavní autori: | , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Freiburg
Wiley Subscription Services, Inc
15.01.2023
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| ISSN: | 0170-4214, 1099-1476 |
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| Abstract | This work proposes a modified forward‐backward splitting algorithm combining an inertial technique for solving the monotone variational inclusion problem. The weak convergence theorem is established under some suitable conditions in Hilbert space, and a new step size is presented for our algorithm to speed up the convergence. We give an example and numerical results for supporting our main theorem in infinite dimensional spaces. We also provide an application to predict breast cancer by using our proposed algorithm for updating the optimal weight in machine learning. Moreover, we use the Wisconsin original breast cancer data set as a training set to show efficiency comparing with the other three algorithms in terms of three key parameters, namely, accuracy, recall, and precision. |
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| AbstractList | This work proposes a modified forward‐backward splitting algorithm combining an inertial technique for solving the monotone variational inclusion problem. The weak convergence theorem is established under some suitable conditions in Hilbert space, and a new step size is presented for our algorithm to speed up the convergence. We give an example and numerical results for supporting our main theorem in infinite dimensional spaces. We also provide an application to predict breast cancer by using our proposed algorithm for updating the optimal weight in machine learning. Moreover, we use the Wisconsin original breast cancer data set as a training set to show efficiency comparing with the other three algorithms in terms of three key parameters, namely, accuracy, recall, and precision. |
| Author | Peeyada, Pronpat Dutta, Hemen Cholamjiak, Watcharaporn Shiangjen, Kanokwatt |
| Author_xml | – sequence: 1 givenname: Pronpat surname: Peeyada fullname: Peeyada, Pronpat organization: University of Phayao – sequence: 2 givenname: Hemen orcidid: 0000-0003-2765-2386 surname: Dutta fullname: Dutta, Hemen organization: Gauhati University – sequence: 3 givenname: Kanokwatt surname: Shiangjen fullname: Shiangjen, Kanokwatt organization: University of Phayao – sequence: 4 givenname: Watcharaporn orcidid: 0000-0002-8563-017X surname: Cholamjiak fullname: Cholamjiak, Watcharaporn email: c-wchp007@hotmail.com organization: University of Phayao |
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| Cites_doi | 10.1007/s10208-013-9150-3 10.1111/j.2517-6161.1996.tb02080.x 10.1016/j.ins.2019.12.045 10.1137/0314056 10.1073/pnas.54.4.1041 10.1023/A:1011253113155 10.1137/050626090 10.1007/978-1-4419-9467-7 10.1016/0041-5553(64)90137-5 10.1016/j.cam.2021.113501 10.1016/j.patrec.2017.06.013 10.1080/10556788.2016.1214959 10.1137/S0363012998338806 10.1007/s11222-009-9153-8 10.1186/s13660-014-0535-x 10.1016/j.compbiomed.2012.10.003 10.1007/s10489-017-1110-1 10.1155/2011/971479 10.1016/j.neucom.2008.01.003 10.1016/j.asoc.2019.105740 10.1137/080716542 10.3390/math8061007 10.1016/j.jmaa.2005.03.002 10.1016/S0898-1221(98)00067-4 10.1016/S0377-0427(02)00906-8 10.1007/s11784-018-0526-5 10.1016/j.camwa.2005.06.001 |
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| SubjectTerms | Algorithms breast cancer Convergence data classification forward‐backward algorithm Hilbert space inertial method Machine learning monotone inclusion Splitting Theorems |
| Title | A modified forward‐backward splitting methods for the sum of two monotone operators with applications to breast cancer prediction |
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