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
Hlavní autori: Peeyada, Pronpat, Dutta, Hemen, Shiangjen, Kanokwatt, Cholamjiak, Watcharaporn
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Freiburg Wiley Subscription Services, Inc 15.01.2023
Predmet:
Algorithms
breast cancer
Convergence
data classification
forward‐backward algorithm
Hilbert space
inertial method
Machine learning
monotone inclusion
Splitting
Theorems
ISSN:0170-4214, 1099-1476
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Shrnutí: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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0170-4214
1099-1476
DOI:10.1002/mma.8578