DCSVM: fast multi-class classification using support vector machines

Don, Duleep Rathgamage; Iacob, Ionut E.

doi:10.1007/s13042-019-00984-9

Cited by 20 publications

(5 citation statements)

References 12 publications

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“…Individual binary classification issue locate the hyperplane, which classifies the features into different classes. The SVM capacity to identify the hyperplane with outliers simplifies it at low SNR [19], [20]. Due to the fact that the magnitude and phase features dependent on the elevation and azimuth, two different algorithms of SVM are utilized to estimate the DoA.…”

Section: Ijeei Issn: 2089-3272 mentioning

confidence: 99%

A Multiclass Support Vector Machine Based Direction-of-Arrival Estimation Technique using Spherical Antenna Array with Undefined Mutual Coupling

Famoriji,

Shongwe

2024

IJEEI

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In antenna array signal processing, estimating the direction-of-arrival (DoA) remains a challenge and basic problem. In this paper, a DoA estimation technique using support vector machine (SVM) classification is developed using spherical antenna array (SAA). The source signal impinging on SAA is decomposed using spherical harmonics (SH). Both magnitude and phase features are computed from the decomposed SH signals. The magnitude and phase features are classified into DoA classes using multi-class SVM (MC-SVM) algorithm. Due to the deterministic and non-probabilistic nature of SVM algorithm, it exhibits high computational speed and less complex than the neural network-dependent learning algorithms. Numerical experiments and experimental measured data (generally accepted ground to test any method) are used to evaluate the performance of the proposed technique. The developed algorithm exhibit high level of robustness at different signal-tonoise ratios (SNR) in the estimation of DoA. Root mean square error (RMSE) performance metrics is employed in the analysis of the proposed method against the state-of-the-art. The results obtained are motivating enough for the deployment of the proposed algorithm in practical scenarios.

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Section: Ijeei Issn: 2089-3272 mentioning

confidence: 99%

A Multiclass Support Vector Machine Based Direction-of-Arrival Estimation Technique using Spherical Antenna Array with Undefined Mutual Coupling

Famoriji,

Shongwe

2024

IJEEI

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“…However, NB and k-NN belong to Bayes (probabilistic) and nearest neighbours respectively [6,7]. The study by Xindong et al also reported that SVM, C4.5, k-NN, AdaBoost, and Naïve Bayes are the most influential classifiers [31,32,33,34] of data mining placed in the top 10 positions [23]. Before using them for classification, the classifiers are validated using 10-fold cross validation.…”

Section: Performance Comparison With Other Classifiersmentioning

confidence: 99%

A Novel Method to Improve the Efficiency of Classification Phase of a Decision Tree

Muneiah

Rao

2020

ELCVIA

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So far, most of the research on classification algorithms in machine learning has been focused only on improving the training speed and further improving the technical performance evaluation measures of the constructed models. There is no focus on improving the runtime efficiency of the classification phase which is much required in some critical applications. In this paper, we are considering the computation complexity of a decision tree's classification phase as the major criterion. A novel approach has been proposed to predict the class label of an unseen instance using the decision tree in less time than the regular tree traversal method. In the proposed method, the constructed decision tree is represented in the form of arrays. Then, the process of finding the class label is carried out by performing the bitwise operations between the elements of the arrays and test instance. Empirical results on various UCI data sets proved that the proposed method outperforms the standard method and five other benchmark classifiers and its classification is at least four times faster than the regular method.

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“…In the former, the problem is to distinguish between two classes, while in the latter there are more than two classes. The multi-class classification problem is considerably more complex because a decision boundary needs to be established to distinguish among many classes [15]. Multi-class classification tasks are widely used in many real-world applications, such as sentiment classification [2,6,31,42], fault diagnosis [28,34], and medical treatment [5,32,36].…”

Section: Introductionmentioning

confidence: 99%

DRCW-FRkNN-OVO: distance-based related competence weighting based on fixed radius k nearest neighbour for one-vs-one scheme

Zhang

Luo

Zhou

2021

Int. J. Mach. Learn. & Cyber.

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The one-versus-one (OVO) binarization decomposition scheme is considered as one of the most effective techniques to deal with multi-class classification problems. Its inherent mechanism is to use the "divide-and-conquer" strategy to decompose the multi-class classification problem into as many pairs of easier-to-solve binary sub-problems as possible. One common issue in the OVO scheme is that of non-competent classifiers. In this study, we proposed a novel OVO scheme strategy, named DRCW-FRkNN-OVO, to reduce the negative effect of non-competent classifiers. Specifically, we focused on the definition of region of competence, which plays a crucial role in managing the non-competent classifiers. To overcome the issue of skew and sparse distribution during the management of non-competent classifiers, we developed a relative competence weighting combination method via the fixed radius nearest neighbour search to find the local region within each class for the query sample. Our proposed DRCW-FRkNN-OVO is tested on 30 real-world multi-class datasets compared with several well-known related works. Experimental results supported by thorough statistical analysis confirmed the effectiveness and robustness of our proposed method.

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DCSVM: fast multi-class classification using support vector machines

Cited by 20 publications

References 12 publications

A Multiclass Support Vector Machine Based Direction-of-Arrival Estimation Technique using Spherical Antenna Array with Undefined Mutual Coupling

A Multiclass Support Vector Machine Based Direction-of-Arrival Estimation Technique using Spherical Antenna Array with Undefined Mutual Coupling

A Novel Method to Improve the Efficiency of Classification Phase of a Decision Tree

DRCW-FRkNN-OVO: distance-based related competence weighting based on fixed radius k nearest neighbour for one-vs-one scheme

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