Sparse and heuristic support vector machine for binary classifier and regressor fusion

classification is the problem of identifying a set of categories to a new comments.To improve the efficiency of the code,quality metrics are applied for evaluation.The binary classifier,predicts the false positive rates with lesser accuracy,and limited number of classes only to predict the accuracy for classifier.To address this problem,support vector machine classifier is used,which helps in detecting the false positive rates,improving code quality and the accuracy will also increased.

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Vehicle Classification Algorithm Based on Fuzzy SVM Models

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.444-445.841 ◽

2013 ◽

Vol 444-445 ◽

pp. 841-848

Author(s):

Yi Chen ◽

Yu Hui Li ◽

Fan Zhang ◽

Feng Zhou

Keyword(s):

Support Vector Machine ◽

Classification Algorithm ◽

Vehicle Classification ◽

Support Vector ◽

Binary Classifier ◽

Sample Problem ◽

Fuzzy Support Vector Machine ◽

High Classification Accuracy ◽

The Difference

As a typical binary classifier, its an inseparable sample problem about the Support Vector Machine (SVM) when processing the classification of the multi-class vehicle models. Since the SVM can not estimate the effect size of the samples classification accurately, and then reduces the classification generalization ability. In this paper, a fuzzy Support Vector Machine (FSVM) classification algorithm is applied to vehicle classification. According to the difference of the contribution which the vehicle characteristics make to the classification, the appropriate degree of membership is given, and the algorithm improves the vehicle models classification ability of the traditional SVM effectively. The experimental results show that the new method, compared with the existing vehicle classification method, is feasible, effective, and with a high classification accuracy

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Efficient Binary Classifier for Prediction of Diabetes Using Data Preprocessing and Support Vector Machine

Advances in Intelligent Systems and Computing - Proceedings of the 3rd International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA) 2014 ◽

10.1007/978-3-319-11933-5_15 ◽

2015 ◽

pp. 131-140

Author(s):

Madhavi Pradhan ◽

G. R. Bamnote

Keyword(s):

Support Vector Machine ◽

Data Preprocessing ◽

Support Vector ◽

Binary Classifier ◽

Using Data ◽

Prediction Of Diabetes

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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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