Improving accuracy in astrocytomas grading by integrating a robust least squares mapping driven support vector machine classifier into a two level grade classification scheme

In view of the complex and changeable operating environment of pumped storage power stations and the noise and outliers in the modeling data, this study proposes a sparse robust least squares support vector machine (LSSVM) model based on the hybrid backtracking search algorithm for the model identification of a pumped turbine governing system. By introducing the maximum linearly independent set, the sparsity of the support vectors of the LSSVM model are realized, and the complexity is reduced. The robustness of the identification model to noise and outliers is enhanced using the weighted function based on improved normal distribution. In order to further improve the accuracy and generalization performance of the sparse robust LSSVM identification model, the model input variables, the kernel parameters, and the regularization parameters are optimized synchronously using a binary-real coded backtracking search algorithm. Experiments on two benchmark problems and a real-world application of a pumped turbine governing system in a pumped storage power station in China show that the proposed sparse robust LSSVM model optimized by the hybrid backtracking search algorithm can not only obtain higher identification accuracy, it also has better robustness and a higher generalization performance compared with the other existing models.

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Dempster–Shafer theory-based robust least squares support vector machine for stochastic modelling

Neurocomputing ◽

10.1016/j.neucom.2015.11.081 ◽

2016 ◽

Vol 182 ◽

pp. 145-153 ◽

Cited By ~ 9

Author(s):

Chuang Zhou ◽

XinJiang Lu ◽

MingHui Huang

Keyword(s):

Support Vector Machine ◽

Least Squares ◽

Stochastic Modelling ◽

Support Vector ◽

Dempster Shafer Theory ◽

Robust Least Squares ◽

Shafer Theory

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A sparse least squares support vector machine classifier

2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541) ◽

10.1109/ijcnn.2004.1379967 ◽

2005 ◽

Cited By ~ 24

Author(s):

J. Valyon ◽

G. Horvath

Keyword(s):

Support Vector Machine ◽

Least Squares ◽

Support Vector Machine Classifier ◽

Support Vector

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A MODIFIED LEAST SQUARES SUPPORT VECTOR MACHINE CLASSIFIER WITH APPLICATION TO CREDIT RISK ANALYSIS

International Journal of Information Technology & Decision Making ◽

10.1142/s0219622009003600 ◽

2009 ◽

Vol 08 (04) ◽

pp. 697-710 ◽

Cited By ~ 25

Author(s):

LEAN YU ◽

SHOUYANG WANG ◽

JIE CAO

Keyword(s):

Support Vector Machine ◽

Risk Analysis ◽

Credit Risk ◽

Least Squares ◽

Support Vector Machine Classifier ◽

Support Vector ◽

Original Form ◽

Simple Modification ◽

Credit Risk Analysis

In this paper, a modified least squares support vector machine classifier, called the C-variable least squares support vector machine (C-VLSSVM) classifier, is proposed for credit risk analysis. The main idea of the proposed classifier is based on the prior knowledge that different classes may have different importance for modeling and more weight should be given to classes having more importance. The C-VLSSVM classifier can be obtained by a simple modification of the regularization parameter, based on the least squares support vector machine (LSSVM) classifier, whereby more weight is given to errors in classification of important classes, than to errors in classification of unimportant classes, while keeping the regularized terms in their original form. For illustration purpose, two real-world credit data sets are used to verify the effectiveness of the C-VLSSVM classifier. Experimental results obtained reveal that the proposed C-VLSSVM classifier can produce promising classification results in credit risk analysis, relative to other classifiers listed in this study.

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