scholarly journals Support vector machine for imbalanced microarray dataset classification using ant colony optimization and genetic algorithm

2019 ◽  
Author(s):  
Diana Nurlaily ◽  
Irhamah ◽  
Santi Wulan Purnami ◽  
Heri Kuswanto
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Ant Colony Optimization ◽  
Microarray Dataset ◽  
Ant Colony ◽  
Support Vector

Prediction of acute toxicity of emerging contaminants on the water flea Daphnia magna by Ant Colony Optimization–Support Vector Machine QSTR models

2017 ◽  
Vol 19 (3) ◽  
pp. 438-448 ◽  
Author(s):  
Reza Aalizadeh ◽  
Peter C. von der Ohe ◽  
Nikolaos S. Thomaidis
Keyword(s):  
Support Vector Machine ◽  
Acute Toxicity ◽  
Ant Colony Optimization ◽  
Daphnia Magna ◽  
Emerging Contaminants ◽  
Ant Colony ◽  
Support Vector ◽  
Water Flea

Prediction of acute toxicity towardsDaphnia magnausing Ant Colony Optimization–Support Vector Machine QSTR models.

A Kind of Network Intrusion Detection Method Using Improved Support Vector Machine Based on Ant Colony Algorithm

2012 ◽  
Vol 263-266 ◽  
pp. 2995-2998
Author(s):  
Xiaoqin Zhang ◽  
Guo Jun Jia
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Feature Extraction ◽  
Ant Colony Algorithm ◽  
Small Sample ◽  
Ant Colony ◽  
Support Vector ◽  
Algorithm Selection ◽  
Data Set ◽  
Network Intrusion

Support vector machine (SVM) is suitable for the classification problem which is of small sample, nonlinear, high dimension. SVM in data preprocessing phase, often use genetic algorithm for feature extraction, although it can improve the accuracy of classification. But in feature extraction stage the weak directivity of genetic algorithm impact the time and accuracy of the classification. The ant colony algorithm is used in genetic algorithm selection stage, which is better for the data pretreatment, so as to improve the classification speed and accuracy. The experiment in the KDD99 data set shows that this method is feasible.

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