Improved EM method for internet traffic classification

Liu, Songyin; Hu, Jing; Hao, Shengnan; Song, Tiecheng

doi:10.1109/kst.2016.7440488

Cited by 6 publications

(1 citation statement)

References 11 publications

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“…Existen varios algoritmos de clasificación basados en machine learning, entre los algoritmos más conocidos podemos encontrar las máquinas de vector soporte (SVM) [12], [13], [14], [15], [16] los algoritmos basados en redes neuronales [17], [18], [19], [20] algoritmos basados en árboles de decisión [21], [22], [23] y los algoritmos de agrupamientos tales como K-means y esperanza-maximización (EM) [24], [25].…”

Section: Aplicaciones De La Ia En El Control De Tráficounclassified

Inteligencia artificial para el control de tráfico en redes de datos: Una Revisión

et al. 2022

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El control del tráfico en las redes de datos ha cobrado gran importancia en los últimos tiempos debido al uso masivo que se le está dando a las redes informáticas en distintos ámbitos de la sociedad. Con el fin de realizar un control de tráfico efectivo, se suele hacer uso de diferentes técnicas que permiten, entre otras cosas, clasificar, predecir y monitorear el tráfico de la red. Estas técnicas han ido evolucionando y actualmente se apoyan en métodos de inteligencia artificial, lo cual ha permitido mejorar los resultados obtenidos con las técnicas convencionales. El presente artículo recopila los diferentes aportes realizados por el campo de la inteligencia artificial al mejoramiento de estas técnicas y a la gestión de redes en general. Se describen los aportes realizados en aspectos tales como la seguridad, la predicción y clasificación del tráfico de datos, así como la optimización del ruteo en una red informática.

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Section: Aplicaciones De La Ia En El Control De Tráficounclassified

Inteligencia artificial para el control de tráfico en redes de datos: Una Revisión

et al. 2022

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A new froth image classification method based on the MRMR-SSGMM hybrid model for recognition of reagent dosage condition in the coal flotation process

et al. 2021

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Intelligent separation is a core technology in the transformation, upgradation, and high-quality development of coal. Realising the intelligent recognition and accurate classification of coal flotation froth is a key technology of intelligent separation. At present, the coal flotation process relies on artificial recognition of froth features for adjusting the reagent dosage. However, owing to the low accuracy and subjectivity of artificial recognition, some problems arise, such as reagent wastage and unqualified product quality. Thus, this paper proposes a new froth image classification method based on the maximal-relevance-minimal-redundancy (MR MR)-semi-supervised Gaussian mixture model (SSGMM) hybrid model for recognition of reagent dosage condition in the coal flotation process. First, the features of morphology, colour, and texture are extracted, and the optimal froth image features are screened out using the maximal-relevance-minimal-redundancy (MRMR) feature selection algorithm based on class information. Second, the traditional GMM clusterer is improved, called SSGMM, by introducing a small number of marked samples, the traditional GMM’ problems of unclear training goals, invisible clustering results, and artificially judged clustering results are solved. Then a new hybrid classification model is proposed by combining the MRMR with the modified GMM (SSGMM) which can be named as (MRMR - SSGMM). The optimal froth image features are screened by MRMR to provide the SSGMM classifier. In the process of training and learning the feature samples, using the marked feature samples of froth images to guide the unmarked feature samples. The information of marked feature samples of froth images is mapped to the unmarked feature samples, the classification of the froth images were realised. Finally, the accuracy of the SSGMM classifier is used as the evaluation criterion for the screened features by MRMR. By automatically executing the entire learning process to find the best number of froth image features and the optimal image features, so that the classifier achieves the maximum classification accuracy. Experimental results show that the proposed classification method achieves the best results in accuracy and time, compared with other benchmark classification methods. Application results show that the method can provide reliable guidance for the adjustment of the reagent dosage, realize the accurate and timely control of the reagent dosage, reduce the consumption of the reagent and the incidence of production accidents, and stabilize the product quality in the coal flotation production process.

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A review on machine learning–based approaches for Internet traffic classification

et al. 2020

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Improved EM method for internet traffic classification

Cited by 6 publications

References 11 publications

Inteligencia artificial para el control de tráfico en redes de datos: Una Revisión

Inteligencia artificial para el control de tráfico en redes de datos: Una Revisión

A new froth image classification method based on the MRMR-SSGMM hybrid model for recognition of reagent dosage condition in the coal flotation process

A review on machine learning–based approaches for Internet traffic classification

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