scholarly journals Hybrid ACO-PSO-GA-DE Algorithm for Big Data Classification

2019 ◽  
Vol 8 (2) ◽  
pp. 703-708
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Big Data ◽  
Optimization Problem ◽  
Support Vector ◽  
Exploration And Exploitation ◽  
Swarm Optimization ◽  
Trade Off ◽  
De Algorithm ◽  
Big Data Classification

This paper designs a technique to classify big data efficiently. This work considers the processing of big data as an optimization problem due to the trade-off between accuracy and time and solves this optimization problem by using a meta-heuristic approach. The HAPGD (Hybrid ACO (Ant Colony Optimization), PSO (Particle Swarm Optimization), GA (Genetic Algorithm), and DE (Differential Evolution)) classification algorithm is designed by using the support vector machine (SVM) along with hybrid ACO-PSO-GA-DE algorithm that hybrids exploration capability of ACO with exploitation capability of PSO whose balance is maintained using modified GA. The GA has been modified by using the DE algorithm. The presented technique performs classification efficiently as shown in results on seven datasets using different analysis parameters due to balanced exploration and exploitation search with fast convergence

scholarly journals Classification of corn kernels grades using image analysis and support vector machine

2018 ◽  
Vol 10 (12) ◽  
pp. 168781401881764 ◽  
Author(s):  
Ang Wu ◽  
Juanhua Zhu ◽  
Yuli Yang ◽  
Xinping Liu ◽  
Xiushan Wang ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Back Propagation ◽  
Particle Swarm ◽  
Support Vector ◽  
Grid Search ◽  
Swarm Optimization ◽  
Back Propagation Neural Networks ◽  
Corn Kernels

In order to classify the quality of corn kernels in an affordable, convenient, and accurate manner, a method based on image analysis and support vector machine is proposed. A total of 129 corn kernels with Grade A, Grade B, and Grade C are used for the experiments. Six typical characteristic parameters of samples are extracted as the characteristic groups. Four different classifiers are applied and compared: support vector machine-genetic algorithm, support vector machine-particle swarm optimization, support vector machine-grid search optimization, and back-propagation neural networks. Experimental results show that the support vector machine and back-propagation neural networks without parameter optimization have the same classification accuracy rates of 92.31%. The classification accuracies are improved using the support vector machine optimization algorithms. The average correct classification rates of support vector machine-genetic algorithm and support vector machine-particle swarm optimization are all 97.44%, while the correct classification rate of support vector machine-grid search achieves 94.87%. It is concluded that the support vector machine algorithm based on parameter optimization is superior to back-propagation neural networks algorithm, and the parameter optimization effects of genetic algorithm and particle swarm optimization are better than grid search method. With a relatively small number of samples, the support vector machine-genetic algorithm and support vector machine-particle swarm optimization algorithms can improve the grading accuracy of corn kernels.

A STUDY ON THE ERROR OF DISTRIBUTED ALGORITHMS FOR BIG DATA CLASSIFICATION WITH SVM

2017 ◽  
Vol 58 (3-4) ◽  
pp. 231-237
Author(s):  
CHENG WANG ◽  
FEILONG CAO
Keyword(s):  
Support Vector Machine ◽  
Big Data ◽  
Distributed Algorithm ◽  
Data Classification ◽  
Classification Error ◽  
Support Vector ◽  
Data Sets ◽  
Gaussian Kernels ◽  
Big Data Classification ◽  
The Given

The error of a distributed algorithm for big data classification with a support vector machine (SVM) is analysed in this paper. First, the given big data sets are divided into small subsets, on which the classical SVM with Gaussian kernels is used. Then, the classification error of the SVM for each subset is analysed based on the Tsybakov exponent, geometric noise, and width of the Gaussian kernels. Finally, the whole error of the distributed algorithm is estimated in terms of the error of each subset.

scholarly journals Construction and Practice of the Optimal Smooth Semi-Supervised Support Vector Machine

2015 ◽  
Vol 3 (5) ◽  
pp. 398-410 ◽  
Author(s):  
Xiaodan Zhang ◽  
Ang Li ◽  
Pan Ran
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Unconstrained Optimization ◽  
Numerical Experiments ◽  
Spline Function ◽  
Optimization Problem ◽  
Hybrid Genetic Algorithm ◽  
Experimental Results ◽  
Support Vector ◽  
Unconstrained Optimization Problem

AbstractThe standard semi-supervised support vector machine (S3VM) is an unconstrained optimization problem of non-convex and non-smooth, so many smooth methods are applied for smoothing S3VM. In this paper, a new smooth semi-supervised support vector machine (SS3VM) model , which is based on the biquadratic spline function, is proposed. And, a hybrid Genetic Algorithm (GA)/ SS3VM approach is presented to optimize the parameters of the model. The numerical experiments are performed to test the efficiency of the model. Experimental results show that generally our optimal SS3VM model outperforms other optimal SS3VM models mentioned in this paper.

Analysis of Feature Reduction Techniques for Online News Popularity Prediction

2018 ◽  
Vol 4 (10) ◽  
pp. 6
Author(s):  
Shivangi Bhargava ◽  
Dr. Shivnath Ghosh
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Naive Bayes ◽  
Particle Swarm ◽  
Naïve Bayes ◽  
Online News ◽  
Feature Reduction ◽  
Support Vector ◽  
Swarm Optimization

News popularity is the maximum growth of attention given for particular news article. The popularity of online news depends on various factors such as the number of social media, the number of visitor comments, the number of Likes, etc. It is therefore necessary to build an automatic decision support system to predict the popularity of the news as it will help in business intelligence too. The work presented in this study aims to find the best model to predict the popularity of online news using machine learning methods. In this work, the result analysis is performed by applying Co-relation algorithm, particle swarm optimization and principal component analysis. For performance evaluation support vector machine, naïve bayes, k-nearest neighbor and neural network classifiers are used to classify the popular and unpopular data. From the experimental results, it is observed that support vector machine and naïve bayes outperforms better with co-relation algorithm as well as k-NN and neural network outperforms better with particle swarm optimization.

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