A Novel Inertial Viscosity Algorithm for Bilevel Optimization Problems Applied to Classification Problems
Kobkoon Janngam,
Suthep Suantai,
Yeol Je Cho,
Attapol Kaewkhao,
Rattanakorn Wattanataweekul
Affiliations
Kobkoon Janngam
Graduate Ph.D. Degree Program in Mathematics, Department of Mathematics, Faculty of Science, Chiang Mai University, Chiang Mai 50200, Thailand
Suthep Suantai
Research Center in Optimization and Computational Intelligence for Big Data Prediction, Department of Mathematics, Faculty of Science, Chiang Mai University, Chiang Mai 50200, Thailand
Yeol Je Cho
Department of Mathematics Education, Gyeogsang National University, Jinju 52828, Republic of Korea
Attapol Kaewkhao
Research Center in Optimization and Computational Intelligence for Big Data Prediction, Department of Mathematics, Faculty of Science, Chiang Mai University, Chiang Mai 50200, Thailand
Rattanakorn Wattanataweekul
Department of Mathematics, Statistics and Computer, Faculty of Science, Ubon Ratchathani University, Ubon Ratchathani 34190, Thailand
Fixed-point theory plays many important roles in real-world problems, such as image processing, classification problem, etc. This paper introduces and analyzes a new, accelerated common-fixed-point algorithm using the viscosity approximation method and then employs it to solve convex bilevel optimization problems. The proposed method was applied to data classification with the Diabetes, Heart Disease UCI and Iris datasets. According to the data classification experiment results, the proposed algorithm outperformed the others in the literature.
