Batch Support Vector Machine-Trained Fuzzy Classifier with channel equalization application

This paper proposes a fast fuzzy classifier of multicategory support vector machines (FMSVM) based on support vector domain description (SVDD). The main idea is that the proposed FMSVM is obtained by directly considering all data in one optimization formulation, using a fuzzy membership to each input point. The fuzzy membership is determined by support vector domain description (SVDD). For making support vector machine (SVM) more practical, we use an implement of the modified sequential minimal optimization (SMO) that can quickly solve SVM quadratic programming (QP) problems without any extra matrix storage or the use of numerical QP optimization steps at all. Compared with the existing SVMs, the newly proposed FMSVM that uses the L2-norm in the objective function shows improvement with regards to accuracy of classification and reduction of the effects of noises and outliers. The experiment also shows the efficiency of the modified SMO for expediting the training of SVM.

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Develop Local Fuzzy Classifier to Modify Low-Confidence Output of Global Classifier

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.989-994.1762 ◽

2014 ◽

Vol 989-994 ◽

pp. 1762-1765

Author(s):

Ping Ling ◽

Xiang Sheng Rong ◽

Yong Quan Dong ◽

Guo Sheng Hao

Keyword(s):

Support Vector Machine ◽

Nearest Neighbor ◽

State Of The Art ◽

The State ◽

Support Vector ◽

K Nearest Neighbor ◽

Fuzzy Classifier ◽

Local Classifier

This paper proposes an assembling classifier consisting of a global classifier and a local classifier, named as GCLC. To this end, we present a weighted Support Vector Machine (wSVM) that serves as the global classifier, and a fuzzy k-nearest neighbor (fkNN) that serves as the local one. When a query arrives, wSVM labels it firstly. If the global decision is below some threshold, the local fkNN works to provide an improved decision. Extensive experiments on real datasets demonstrate the performance of GCLC compared with the state of the art.

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Fuzzy classifier based on fuzzy support vector machine

Journal of Intelligent & Fuzzy Systems ◽

10.3233/ifs-130819 ◽

2014 ◽

Vol 26 (1) ◽

pp. 421-430 ◽

Cited By ~ 3

Author(s):

Ai-bing Ji ◽

Songcan Chen ◽

Qiang Hua

Keyword(s):

Support Vector Machine ◽

Support Vector ◽

Fuzzy Support Vector Machine ◽

Fuzzy Classifier

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Support vector machine? – not only for MRI-images

10.1055/s-0039-3403050 ◽

2020 ◽

Author(s):

V Vasilevska ◽

K Schlaaf ◽

H Dobrowolny ◽

G Meyer-Lotz ◽

HG Bernstein ◽

...

Keyword(s):

Support Vector Machine ◽

Support Vector

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KLASIFIKASI SMS SPAM MENGGUNAKAN SUPPORT VECTOR MACHINE

Jurnal Pilar Nusa Mandiri ◽

10.33480/pilar.v15i2.693 ◽

2019 ◽

Vol 15 (2) ◽

pp. 275-280

Author(s):

Agus Setiyono ◽

Hilman F Pardede

Keyword(s):

Data Mining ◽

Support Vector Machine ◽

Decision Tree ◽

Naive Bayes ◽

Naïve Bayes ◽

Support Vector ◽

Spam Detection ◽

Support Vector Machine Algorithm ◽

Data Mining Techniques ◽

To Receive

It is now common for a cellphone to receive spam messages. Great number of received messages making it difficult for human to classify those messages to Spam or no Spam. One way to overcome this problem is to use Data Mining for automatic classifications. In this paper, we investigate various data mining techniques, named Support Vector Machine, Multinomial Naïve Bayes and Decision Tree for automatic spam detection. Our experimental results show that Support Vector Machine algorithm is the best algorithm over three evaluated algorithms. Support Vector Machine achieves 98.33%, while Multinomial Naïve Bayes achieves 98.13% and Decision Tree is at 97.10 % accuracy.

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