An Improved Technique for Pneumonia Infected Patients Image Recognition Based on Combination Algorithm of Smooth Generalized Pinball SVM and Variational Autoencoders

We present a method based on combining a smooth generalized pinball support vector machine (SVM) and variational autoencoders (VAEs) in chest X-ray (CXR) images. We incorporate generalized pinball into the SVM model to address the hinge loss function’s noise sensitivity and resampling instability. S...

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Bibliographic Details
Published in:IEEE access Vol. 10; pp. 107431 - 107445
Main Authors: Ratiphaphongthon, Wachiraphong, Panup, Wanida, Wangkeeree, Rabian
Format: Journal Article
Language:English
Published: Piscataway The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2022
IEEE
Subjects:
Algorithms
Correlation coefficients
Datasets
feature noise
generalized pinball loss
image recognition
Iterative methods
Noise sensitivity
Object recognition
Quasi Newton methods
regularized stochastic BFGS algorithm
Resampling
Smoothing
Support vector machine
Support vector machines
ISSN:2169-3536, 2169-3536
Online Access:Get full text
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Summary:We present a method based on combining a smooth generalized pinball support vector machine (SVM) and variational autoencoders (VAEs) in chest X-ray (CXR) images. We incorporate generalized pinball into the SVM model to address the hinge loss function’s noise sensitivity and resampling instability. Since the generalized pinball loss is a non-differentiable function, the objective function is non-differentiable. To solve the non-differentiable term in the model’s objective function, we construct a smooth approximation function for the generalized pinball loss function so that the model in the original space can be solved directly. More specifically, using the proposed smoothing functions, we employ the smoothing stochastic quasi-Newton method to solve the primal problem. Moreover, we have introduced verified theorems related to our approaches, and the theoretical convergence of our approaches has been proposed. Numerical comparisons between parameter values of several iteration objective functions are also provided to test the model. Experiments with several benchmark datasets show that our method has the best prediction accuracy 64% and 57% from all of binary datasets and multiclass datasets, respectively. Moreover, our method has the best prediction of Matthews correlation coefficient (MCC) 64% from all of datasets.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2022.3212535