One-against-one fuzzy support vector machine classifier: An approach to text categorization

Wang, Tai-Yue; Chiang, Huei-Min

doi:10.1016/j.eswa.2009.01.025

Cited by 37 publications

(14 citation statements)

References 18 publications

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“…3. One Against One (OAO) method: This method is also called as maxwins voting (MWV-SVM) method [48]. This is a category of pairwise classification.…”

Section: Health Data Analysis Phasementioning

confidence: 99%

Vehicular Network based Emergency Data Transmission and Classification for Health Care System using Support Vector Machine

Senapati

Khilar

Dash

et al. 2022

Preprint

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COVID-19 pandemic has created an emergency across the globe. The number of corona positive cases and death cases are still rising across the world. The government of all countries is taking various steps to control the infection of COVID-19. One of the steps to control the spreading of the coronavirus is to quarantine. But the number of active cases at the quarantine center is increasing day by day. Also, the doctors, nurses, and paramedical staff providing service to the people at the quarantine center are getting infected. This demands the automatic and regular monitoring of people at the quarantine center. This paper proposed a novel and automated method for the monitoring of people at the quarantine center by two phases. These are the health data transmission phase and health data analysis phase. The health data transmission phase involves component NIBs, RSUs, and vehicles. For the transmission of data from the quarantine center to the observation center, an effective route is determined using route value. The route-value is dependent on factors like density, shortest path, delay, vehicular data carrying delay, and attenuation. The analysis of health data is done by using multi-class SVM. Finally, simulation results are obtained for the two phases. The simulation results obtained for the health data transmission phase is E2E delay, number of network gaps, and packet delivery ratio. The health data analysis phase determines parameters such as precision, recall, accuracy, etc.

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“…3. One Against One (OAO) method: This method is also called as maxwins voting (MWV-SVM) method [48]. This is a category of pairwise classification.…”

Section: Health Data Analysis Phasementioning

confidence: 99%

Vehicular Network based Emergency Data Transmission and Classification for Health Care System using Support Vector Machine

Senapati

Khilar

Dash

et al. 2022

Preprint

View full text Add to dashboard Cite

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“…In 2007, Twin Support Vector Machine (TWSVM) is proposed by Jayadeva et al [1,2,3]. It obtains nonparallel planes around which the data points of the corresponding class get clustered.…”

Section: Twin Support Vector Machinementioning

confidence: 99%

Feature selected cost-sensitive twin SVM for imbalanced data

Zhang²

2020

MATEC Web Conf.

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1 Zhejiang normal university, jinhua city, zhejiang province, China 2 Keyuan new village, jinzhai road, baohe district, hefei city, anhui province, China Keyword: Twin SVM, cost-sensitive, feature selection, imbalanced data.Abstract. In this paper, we propose a cost-sensitive twin SVM (cs-tsvm) and apply it to imbalanced data. A weight is added to each instance according to its cost of misclassification which is related to its position. In preprocessing part, features are selected by their difference of majority and minority classes. The feature is selected when its difference value is higher than average one. The experiment is conducted on UCI datasets and G-mean, AUC and accuracy are evaluation metrics. The experimental results show that Feature selection with CS-TWSVM is useful for datasets with high dimension.

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“…The classification model for inertinite macerals with the SVM-based classifiers is illustrated in Figure 11. To implement the multi-classification, we construct a classifier group with 28 RBF-SVM classifiers to distinguish eight groups of inertinite macerals based on the one-against-one (1A1) technique and optimize the error parameter (usually designated c) and parameter γ in RBF kernel function by a grid search [33,34]. Besides, 40 of the microscopic samples per group are used for training, and the remaining 20 samples for testing and each classifier is used to distinguish two different classes of inertinite macerals.…”

Section: Experiments Designmentioning

confidence: 99%

The Classification of Inertinite Macerals in Coal Based on the Multifractal Spectrum Method

et al. 2019

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Considering the heterogeneous nature and non-stationary property of inertinite components, we propose a texture description method with a set of multifractal descriptors to identify different macerals with few but effective features. This method is based on the multifractal spectrum calculated from the method of multifractal detrended fluctuation analysis (MF-DFA). Additionally, microscopic images of inertinite macerals were analyzed, which were verified to possess the property of multifractal. Simultaneously, we made an attempt to assess the influences of noise and blur on multifractal descriptors; the multifractal analysis was proven to be robust and immune to image quality. Finally, a classification model with a support vector machine (SVM) was built to distinguish different inertinite macerals from microscopic images of coal. The performance evaluation proves that the proposed descriptors based on multifractal spectrum can be successfully applied in the classification of inertinite macerals. The average classification precision can reach 95.33%, higher than that of description method with gray level co-occurrence matrix (GLCM; about 7.99%).

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One-against-one fuzzy support vector machine classifier: An approach to text categorization

Cited by 37 publications

References 18 publications

Vehicular Network based Emergency Data Transmission and Classification for Health Care System using Support Vector Machine

Vehicular Network based Emergency Data Transmission and Classification for Health Care System using Support Vector Machine

Feature selected cost-sensitive twin SVM for imbalanced data

The Classification of Inertinite Macerals in Coal Based on the Multifractal Spectrum Method

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