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.

Study on Classification Technology of Wool and Cashmere Based on GA-SVM

2011 ◽  
Vol 332-334 ◽  
pp. 1198-1201 ◽  
Author(s):  
Yun Hui Yang ◽  
Yi Ping Ji
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Experimental Results ◽  
Practical Significance ◽  
Support Vector ◽  
Optimization Search

Distinguishing of wool and cashmere is one of the toughest problems in fiber identification area. Support Vector Machine (SVM) was advanced here to classify fibers, and Genetic Algorithm (GA) was used to optimize multi-parameters of SVM simultaneously. Experimental results show that it plays full part of the GA, and accelerates the optimization search of SVM parameters. The model established is of practical significance in identification of wool and cashmere.

A hybrid genetic algorithm–support vector machine approach in the task of forecasting and trading

2013 ◽  
Vol 14 (1) ◽  
pp. 52-71 ◽  
Author(s):  
Christian L Dunis ◽  
Spiros D Likothanassis ◽  
Andreas S Karathanasopoulos ◽  
Georgios S Sermpinis ◽  
Konstantinos A Theofilatos
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Hybrid Genetic Algorithm ◽  
Support Vector

Firepower Assignment of Ship-to-Air Missile System

2014 ◽  
Vol 905 ◽  
pp. 702-705
Author(s):  
Yong Hong Lu ◽  
Ji Hua Dou ◽  
Xing Bao Yang ◽  
Chuan Wei Zhu
Keyword(s):  
Genetic Algorithm ◽  
Global Optimization ◽  
Decision Support ◽  
Optimization Problem ◽  
Hybrid Genetic Algorithm ◽  
Hill Climbing ◽  
Unconstrained Optimization Problem ◽  
Provide Decision Support ◽  
Missile System ◽  
Standard Genetic Algorithm

Hybrid genetic algorithm has been proposed in this paper, which is proposed by combining standard genetic algorithm with hill climbing to solve the unconstrained optimization problem, which can get global optimization results of the firepower assignment, and provide decision support for the firepower assignment.

A HYBRID OF GENETIC ALGORITHM AND SUPPORT VECTOR MACHINE FOR FEATURES SELECTION AND CLASSIFICATION OF GENE EXPRESSION MICROARRAY

2005 ◽  
Vol 05 (01) ◽  
pp. 91-107 ◽  
Author(s):  
MOHD SABERI MOHAMAD ◽  
SAFAAI DERIS ◽  
ROSLI MD ILLIAS
Keyword(s):  
Gene Expression ◽  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Classification Accuracy ◽  
Hybrid Genetic Algorithm ◽  
Support Vector ◽  
Small Subset ◽  
Features Selection ◽  
Gene Expression Microarray ◽  
Expression Microarray

Constantly improving gene expression technology offer the ability to measure the expression levels of thousand of genes in parallel. Gene expression data is expected to significantly aid in the development of efficient cancer diagnosis and classification platforms. Key issue that needs to be addressed is the selection of small number of genes that contribute to a disease from the thousands of genes measured on microarrays that are inherently noisy. This work deals with finding a small subset of informative genes from gene expression microarray data which maximise the classification accuracy. This paper introduces a new algorithm of hybrid Genetic Algorithm and Support Vector Machine for genes selection and classification task. We show that the classification accuracy of the proposed algorithm is superior to a number of current state-of-the-art methods of two widely used benchmark datasets. The informative genes from the best subset are validated and verified by comparing them with the biological results produced from biologist and computer scientist researches in order to explore the biological plausibility.

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 Mapping Mineral Prospectivity Using a Hybrid Genetic Algorithm–Support Vector Machine (GA–SVM) Model

2021 ◽  
Vol 10 (11) ◽  
pp. 766
Author(s):  
Xishihui Du ◽  
Kefa Zhou ◽  
Yao Cui ◽  
Jinlin Wang ◽  
Shuguang Zhou
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Hybrid Genetic Algorithm ◽  
Geochemical Data ◽  
Support Vector ◽  
Adaptive Optimization ◽  
Spatial Efficiency ◽  
Svm Model ◽  
Mineral Prospectivity ◽  
Optimization Search

Machine learning (ML) as a powerful data-driven method is widely used for mineral prospectivity mapping. This study employs a hybrid of the genetic algorithm (GA) and support vector machine (SVM) model to map prospective areas for Au deposits in Karamay, northwest China. In the proposed method, GA is used as an adaptive optimization search method to optimize the SVM parameters that result in the best fitness. After obtaining evidence layers from geological and geochemical data, GA–SVM models trained using different training datasets were applied to discriminate between prospective and non-prospective areas for Au deposits, and to produce prospectivity maps for mineral exploration. The F1 score and spatial efficiency of classification were calculated to objectively evaluate the performance of each prospectivity model. The best model predicted 95.83% of the known Au deposits within prospective areas, occupying 35.68% of the study area. The results demonstrate the effectiveness of the GA–SVM model as a tool for mapping mineral prospectivity.

Sign in / Sign up

Export Citation Format

Share Document