Random vector functional link with ε-insensitive Huber loss function for biomedical data classification. (March 2022)

• Record Type: Journal Article
• Authors: Hazarika, Barenya Bikash; Gupta, Deepak
• Is Part Of: Computer methods and programs in biomedicine. Volume 215(2022)
• Publication Date: 2022-03
• Subjects: Random vector functional link -- Classification -- Noisy data -- Ε-insensitive Huber loss; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2022.106622
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: An ε-insensitive Huber loss function based RVFL (ε-HRVFL) is proposed for biomedical data classification. A simple functional iterative technique has been used to find the solutions of ε-HRVFL. Simulations have been performed on a few biomedical datasets with and without noise. Experiments are also performed on a few real-world non-biomedical datasets with and without noise. Abstract: Background and objective: Biomedical data classification has been a trending topic among researchers during the last decade. Biomedical datasets may contain several features noises. Hence, the conventional machine learning model cannot efficiently handle the presence of noise in datasets. Among the several machine learning model, the random vector functional link (RVFL) is one of the most popular and efficient models for task related to both classification and regression. Despite its excellent classification performance, its performance degrades while dealing with the datasets with noise. Researchers are searching for powerful models to minimize the influence of noise in datasets. Therefore, to enhance the classification ability of RVFL on noisy datasets, this paper suggests a novel random vector functional link with ε-insensitive Huber loss function (ε-HRVFL) for biomedical data classification problems. Methods: The optimization problem of ε-HRVFL is reformulated as strongly convex minimization problems with a simple function iterative approach to find solutions. To have a better understanding of the scope of the biomedical data classification problem and potential solutions, we conducted experiments with three different types of label noise in biomedical datasets as well as a few non-biomedical datasets. The classification accuracy of the proposed ε-HRVFL model is compared statistically using Friedman test with the support vector machine, extreme learning machine with radial basis function (RBF) and sigmoid activation functions and RVFL with RBF and sigmoid activation functions. Results: For non-biomedical datasets, the proposed model showed the highest accuracy of 98.1332%. Moreover, for the biomedical datasets, the proposed model showed the best accuracy of 96.5229%. The proposed ε-HRVFL model with sigmoid activation function reveals the best mean ranks among the reported classifiers for both, biomedical and non-biomedical datasets. Conclusion: Numerical results show the applicability of the proposed ε-HRVFL model. In future, the proposed ε-HRVFL can be developed to solve multiclass biomedical data classification problems. Moreover, ε-insensitive asymmetric Huber loss function based RVFL model can be developed for dealing more efficiently with these noisy biomedical datasets.