An Adaptive Concentration Selection Model for Spam Detection

2014 ◽  
pp. 223-233 ◽  
Author(s):  
Yang Gao ◽  
Guyue Mi ◽  
Ying Tan
Keyword(s):  
Selection Model ◽  
Spam Detection

OPTIMIZATION OF ATTRIBUTE SELECTION MODEL USING BIO-INSPIRED ALGORITHMS

2019 ◽  
Vol 18 (1) ◽  
pp. 35-55
Author(s):  
Mohammad Aizat Basir ◽  
Yuhanis Yusof ◽  
Mohamed Saifullah Hussin
Keyword(s):  
Selection Model ◽  
Attribute Selection

KLASIFIKASI SMS SPAM MENGGUNAKAN SUPPORT VECTOR MACHINE

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.

Adaptive Component Replica Selection Model and Algorithms

2008 ◽  
Vol 19 (5) ◽  
pp. 1212-1223 ◽  
Author(s):  
Lin ZUO
Keyword(s):  
Selection Model ◽  
Replica Selection

A Service Selection Model Using Mixed Intelligent Optimization

2010 ◽  
Vol 33 (11) ◽  
pp. 2116-2125 ◽  
Author(s):  
Yan-Ping CHEN ◽  
Jian-Ke ZHANG ◽  
Jia-Ze SUN ◽  
Qing-Hua ZHENG ◽  
Zheng-Zhi LI
Keyword(s):  
Service Selection ◽  
Selection Model ◽  
Intelligent Optimization

A Sequential Recommendation - Selection Model

2019 ◽  
Author(s):  
Ningyuan Chen ◽  
Guillermo Gallego ◽  
Pin Gao ◽  
Anran Li
Keyword(s):  
Selection Model
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