Work

Optimization model and application of linear and nonlinear MBSVM based on pinball loss function

Year: 2024

Type: article

Abstract: The multiple birth support vector machine (MBSVM) typically utilizes a hinge loss function, which is susceptible to noise sensitivity and instability during resampling. To overcome these issues, we introduce two models: linear and nonlinear MBSVM models, both utilizing a pinball loss function. The main goal of these models is to improve the classification performance and noise resilience of the Pin-MBSVM by optimizing the maximum quantile distance. Additionally, to decrease the computation time for these models, we provide an in-depth discussion of a fast algorithm based on the successive overrelaxation (SOR) iteration method. In experiments, we test the Pin-MBSVM algorithm using both UCI datasets and synthetic datasets, comparing its performance with OVO-TWSVM, OVA-TWSVM and MBSVM. The results show that our methods successfully reduce the noise sensitivity and resampling instability found in traditional MBSVM while preserving the model’s computational efficiency. Finally, the effectiveness of our model was validated using the Friedman test and Bonferroni–Dunn test.

Source: Mathematical and Computer Modelling of Dynamical Systems

DOI: 10.1080/13873954.2024.2424854

Authors: Linfeng Dai, Longwei Chen, Min Luo

Institution: Yunnan University of Finance And Economics

Language: English

Topic: Machine Learning and Data Classification

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,533

Funder: Research and Development