A Weighted Support Vector Machine for Data Classification

Yang, Xulei; Song, Qing; Wang, Yue

doi:10.1142/s0218001407005703

Cited by 171 publications

(83 citation statements)

References 17 publications

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“…The training samples in an LSVM can be used in the calibration process with varying levels of importance [47]. In geographical problems, this importance can be defined by weights that are assigned to training samples based on their spatial similarity to the focal location of calibration.…”

Section: Local Support Vector Machinesmentioning

confidence: 99%

“…Considering the generalisation error, Ralaivola and d'Alché-Buc [46] presented a methodology for training SVMs known as incremental learning, which is based on the number of nearest neighbours. Yang et al [47] proposed a weighted SVM model that was used to locally classify data based on the similarity between the training and testing data sets [48,49].…”

Section: Introductionmentioning

confidence: 99%

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Integration of Local and Global Support Vector Machines to Improve Urban Growth Modelling

Mirbagheri

Alimohammadi

2018

IJGI

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Abstract:The use of local information for the classification and modelling of spatial variables has increased with the application of statistical and machine learning algorithms, such as support vector machines (SVMs). This study presents a new local SVM (LSVM) model that was developed to model the probability of urban development and simulate urban growth in a subregion in the southwestern suburb of the Tehran metropolitan area, Iran, for the periods of 1992-1996 and 1996-2002. Based on the focal training sample, the model was calibrated using the cross-validation method, and the optimal bandwidth was determined. The results were compared with those of a nonlinear global SVM (GSVM) model that was calibrated based on the ten-fold cross-validation method. This study then evaluated an integrated SVM model (LGSVM) obtained based on a weighted combination of the local and global urban development probabilities. A comparison of the probability maps showed a higher accuracy for the LGSVM than for either the LSVM or GSVM model. To assess the performance of the LSVM, GSVM and LGSVM models in the simulation of urban growth, probability maps were employed as the transition rules for urban cellular automata. The results show that a trade-off between local and global SVM models can enhance the performance of urban growth modelling.

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Section: Local Support Vector Machinesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Integration of Local and Global Support Vector Machines to Improve Urban Growth Modelling

Mirbagheri

Alimohammadi

2018

IJGI

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“…Sampling methods focus on balancing the distributions of data points between classes while cost-sensitive learning methods take into account the costs associated with misclassifying data points [45][46][47][48][49][50][51] . It considers the variable cost of a misclassification of the different classes.…”

Section: Cost -Sensitive Solutionsmentioning

confidence: 99%

An effective Weighted Multi-class Least Squares Twin Support Vector Machine for Imbalanced data classification

Tomar¹,

Agarwal²

2015

IJCIS

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The performance of machine learning algorithms is affected by the imbalanced distribution of data among classes. This issue is crucial in various practical problem domains, for example, in medical diagnosis, network intrusion, fraud detection etc. Most efforts so far are mainly focused upon binary class imbalance problem. However, the class imbalance problem is also reported in multi-class scenario. The solutions proposed by the researchers for twoclass scenario are not applicable to multi-class domains. So, in this paper, we have developed an effective Weighted Multi-class Least Squares Twin Support Vector Machine (WMLSTSVM) approach to address the problem of imbalanced data classification for multi class. This research work employs appropriate weight setting in loss function, e.g. it adjusts the cost of error for imbalanced data in order to control the sensitivity of the classifier. In order to prove the validity of the proposed approach, the experiment has been performed on fifteen benchmark datasets. The performance of proposed WMLSTSVM is analyzed and compared with some other SVMs and TWSVMs and it is observed that our proposed approach outperforms all of them. The proposed approach is statistically analyzed by using non-parametric Wilcoxon signed rank and Friedman tests.

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“…Examples include assigning a weight to each class or learning from one class (recognition-based) rather than two classes (discrimination-based) [17]. Weighted Support Vector Machines (SVMs) [18] assign distinct weights to data samples so that the training algorithm learns the decision surface according to the relative importance of data points in the training dataset. Fuzzy Support Vector Machines [19] is a version of weighted SVMs that applies a fuzzy membership to each input sample and reformulates the SVMs so that input points make different contributions to the learning of decision surface.…”

Section: Understanding Bgp Datamentioning

confidence: 99%

Classification of BGP anomalies using decision trees and fuzzy rough sets

Xing

Hua

et al. 2014

2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

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Abstract-Border Gateway Protocol (BGP) is the core component of the Internet's routing infrastructure. Abnormal routing behavior impairs global Internet connectivity and stability. Hence, designing and implementing anomaly detection algorithms is important for improving performance of routing protocols. While various machine learning techniques may be employed to detect BGP anomalies, their performance strongly depends on the employed learning algorithms. These techniques have multiple variants that often work well for detecting a particular anomaly. In this paper, we use the decision tree and fuzzy rough set methods for feature selection. Decision tree and extreme learning machine classification techniques are then used to maximize the accuracy of detecting BGP anomalies. The proposed techniques are tested using Internet traffic traces.

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A Weighted Support Vector Machine for Data Classification

Cited by 171 publications

References 17 publications

Integration of Local and Global Support Vector Machines to Improve Urban Growth Modelling

Integration of Local and Global Support Vector Machines to Improve Urban Growth Modelling

An effective Weighted Multi-class Least Squares Twin Support Vector Machine for Imbalanced data classification

Classification of BGP anomalies using decision trees and fuzzy rough sets

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