TY - JOUR

T1 - Case-Based Statistical Learning

T2 - A Non-Parametric Implementation with a Conditional-Error Rate SVM

AU - Gorriz, J. M.

AU - Ramirez, J.

AU - Suckling, J.

AU - Illan, Ignacio Alvarez

AU - Ortiz, Andres

AU - Martinez-Murcia, F. J.

AU - Segovia, Fermin

AU - Salas-Gonzalez, D.

AU - Wang, Shuihua

N1 - Publisher Copyright:
© 2013 IEEE.

PY - 2017

Y1 - 2017

N2 - Machine learning has been successfully applied to many areas of science and engineering. Some examples include time series prediction, optical character recognition, signal and image classification in biomedical applications for diagnosis and prognosis and so on. In the theory of semi-supervised learning, we have a training set and an unlabeled data, that are employed to fit a prediction model or learner, with the help of an iterative algorithm, such as the expectation-maximization algorithm. In this paper, a novel non-parametric approach of the so-called case-based statistical learning is proposed in a low-dimensional classification problem. This supervised feature selection scheme analyzes the discrete set of outcomes in the classification problem by hypothesis-testing and makes assumptions on these outcome values to obtain the most likely prediction model at the training stage. A novel prediction model is described in terms of the output scores of a confidence-based support vector machine classifier under class-hypothesis testing. To have a more accurate prediction by considering the unlabeled points, the distribution of unlabeled examples must be relevant for the classification problem. The estimation of the error rates from a well-trained support vector machines allows us to propose a non-parametric approach avoiding the use of Gaussian density function-based models in the likelihood ratio test.

AB - Machine learning has been successfully applied to many areas of science and engineering. Some examples include time series prediction, optical character recognition, signal and image classification in biomedical applications for diagnosis and prognosis and so on. In the theory of semi-supervised learning, we have a training set and an unlabeled data, that are employed to fit a prediction model or learner, with the help of an iterative algorithm, such as the expectation-maximization algorithm. In this paper, a novel non-parametric approach of the so-called case-based statistical learning is proposed in a low-dimensional classification problem. This supervised feature selection scheme analyzes the discrete set of outcomes in the classification problem by hypothesis-testing and makes assumptions on these outcome values to obtain the most likely prediction model at the training stage. A novel prediction model is described in terms of the output scores of a confidence-based support vector machine classifier under class-hypothesis testing. To have a more accurate prediction by considering the unlabeled points, the distribution of unlabeled examples must be relevant for the classification problem. The estimation of the error rates from a well-trained support vector machines allows us to propose a non-parametric approach avoiding the use of Gaussian density function-based models in the likelihood ratio test.

KW - Statistical learning and decision theory

KW - conditional-error rate

KW - hypothesis testing

KW - partial least squares

KW - support vector machines (SVM)

UR - http://www.scopus.com/inward/record.url?scp=85026915858&partnerID=8YFLogxK

U2 - 10.1109/ACCESS.2017.2714579

DO - 10.1109/ACCESS.2017.2714579

M3 - Article

AN - SCOPUS:85026915858

SN - 2169-3536

VL - 5

SP - 11468

EP - 11478

JO - IEEE Access

JF - IEEE Access

M1 - 7945239

ER -