Robust twin support vector machine for pattern classification

Qi, Zhiquan; Tian, Yingjie; Shi, Yong

doi:10.1016/j.patcog.2012.06.019

Cited by 273 publications

(77 citation statements)

References 30 publications

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“…which acts as decision boundary and is evaluated thereby using the function Φ that maps which is in higher dimension [14]. The distance is maximized for the set of data points consistent on the training set with hyperplane characterized by ( , ) w b .…”

Section: Support Vector Machines With Kernel Evaluationmentioning

confidence: 99%

“…Unauthenticated Download Date | 5/11/18 4:44 PM which acts as decision boundary and is evaluated thereby using the function  that maps x to S space which is in higher dimension [14]. The distance is maximized for the set of data points ( ) The solution to the above problem is established using the Lagrangian formulation and it is shown that …”

Section: Support Vector Machines With Kernel Evaluationmentioning

confidence: 99%

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Friedman and Wilcoxon Evaluations Comparing SVM, Bagging, Boosting, K-NN and Decision Tree Classifiers

Biju

Prashanth²

2017

Journal of Applied Computer Science Methods

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This paper describes a number of experiments to compare and validate the performance of machine learning classifiers. Creating machine learning models for data with wide varieties has huge applications in predictive modelling across multiple domain of science. This work reviews state of the art techniques in machine learning classifiers methods with several extent of magnitude in statistics and key findings that will be helpful in establishing best methodological practices for class predictions. Comprehensive comparative review analysis with statistical validations for various machine learning algorithm for SVM, Bagging, Boosting, Decision Trees and Nearest Neighborhood algorithm on multiple data sets is carried out. Focus on the statistical analysis of the results using Friedman-Test and Wilcoxon Test as well as other interpretative metrics like classification rate, ROC, F-measure are evaluated to benchmark results.

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Section: Support Vector Machines With Kernel Evaluationmentioning

confidence: 99%

Section: Support Vector Machines With Kernel Evaluationmentioning

confidence: 99%

Friedman and Wilcoxon Evaluations Comparing SVM, Bagging, Boosting, K-NN and Decision Tree Classifiers

Biju

Prashanth²

2017

Journal of Applied Computer Science Methods

View full text Add to dashboard Cite

show abstract

“…If ε-insensitive loss function is utilized, errors between -and + are going to be ignored. If C=Inf is set, regression curve will follow the training data inside the margin which is determined by (Qi, Tian & Shi, 2013). The related equation can be observed in (6).…”

Section: Svr (Support Vector Regression)mentioning

confidence: 99%

Fuzzified Pipes Dataset to Predict Failure Rates by Hybrid SVR-PSO Algorithm

Soltani¹,

Kalanaki²,

Soltani³

2016

MAS

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This paper proposes a Support Vector Regression (SVR) based on Fuzzified Input-output Variables which has good comprehensibility as well as satisfactory generalization capability. SVM provides a mechanism to predict data from training ones. Then, results from proposed Fuzzified SVR-PSO (FSVR-PSO) model are compared with other methods; comparative tests are performed using pipe failures data. The analysis and the experimental results show this method has high comprehensibility as well as satisfactory generalization capability.

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“…There are many methods of constructing the robust SVMs such as [128,129]. Robust TWSVM (R-TWSVM) [130] via second order cone programming (SOCP) formulations for classification was an improved extension of TWSVM, since there were only inner products about inputs in the dual problems which kernel trick can bey directly applied for nonlinear cases and the inverse of matrices were not needed any more. The prior knowledge in the form of multiple polyhedral sets were incorporated into the linear TWSVM and LSTWSVM, termed as knowledge based TWSVM (KBTWSVM) and knowledge based LSTWSVM (KBLSTWSVM), were formulated in [131].…”

Section: Knowledge-based Twsvmsmentioning

confidence: 99%

Review on: Twin Support Vector Machines

Tian

2014

Ann. Data. Sci.

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Twin support vector machine (TWSVM), an useful extension of the traditional SVM, becomes the current researching hot spot in machine learning during the last few years. For the binary classification problem, the basic idea of TWSVM is to seek two nonparallel proximal hyperplanes such that each hyperplane is closer to one of the two classes and is at least one distance from the other. TWSVM has lower computational complexity and better generalization ability, therefore in the last few years it has been studied extensively and developed rapidly. Considering the many variants of TWSVM, a systematic survey is needed and helpful to understand and use this family of data mining techniques more easily. The purpose of this paper is to closely review TWSVMs and provide an insightful understanding of current developments, at the same time point out their limitations and highlight the major opportunities and challenges, as well as potential important research directions.

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Robust twin support vector machine for pattern classification

Cited by 273 publications

References 30 publications

Friedman and Wilcoxon Evaluations Comparing SVM, Bagging, Boosting, K-NN and Decision Tree Classifiers

Friedman and Wilcoxon Evaluations Comparing SVM, Bagging, Boosting, K-NN and Decision Tree Classifiers

Fuzzified Pipes Dataset to Predict Failure Rates by Hybrid SVR-PSO Algorithm

Review on: Twin Support Vector Machines

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