HYBRID OF GENETIC ALGORITHM AND SIMULATED ANNEALING FOR SUPPORT VECTOR REGRESSION OPTIMIZATION IN RAINFALL FORECASTING

2013 ◽  
Vol 12 (02) ◽  
pp. 1350012 ◽  
Author(s):  
CHANGMING ZHU ◽  
JIANSHENG WU
Keyword(s):  
Genetic Algorithm ◽  
Kernel Function ◽  
Subset Selection ◽  
Feature Subset Selection ◽  
Support Vector ◽  
Feature Subset ◽  
Parameter Setting ◽  
Rainfall Forecasting ◽  
Input Feature ◽  
Kernel Parameter

Accurate forecasting of rainfall has been one of the most important issues in hydrological research such as river training works and design of flood warning systems. Support vector regression (SVR) is a popular regression method in rainfall forecasting. Type of kernel function and kernel parameter setting in the SVR traing procedure, along with the input feature subset selection, significantly influence regression accuracy. In this paper, an effective hybrid optimization strategy by combining the strengths of genetic algorithm (GA) and simulated annealing (SA), is employed to simultaneously optimize the input feature subset selection, the type of kernel function and the kernel parameter setting of SVR, namely GASA–SVR. The developed GASA–SVR model is being applied for monthly rainfall forecasting in Guilin of Guangxi. The GA is carried out as a main frame of this hybrid algorithm while SA is used as a local search strategy to help GA jump out of local optima and avoid sinking into the local optimal solution early. Compared with SVR, pure GA–SVR and HGA–SVR, results show that the hybrid GASA–SVR model can correctly select the discriminating input features subset, successfully identify the optimal type of kernel function and all the optimal values of the parameters of SVR with the lowest prediction error values in rainfall forecasting, can also significantly improve the rainfall forecasting accuracy. Experimental results reveal that the predictions using the proposed approach are consistently better than those obtained using the other methods presented in this study in terms of the same measurements. Those results show that the proposed GASA–SVR model provides a promising alternative to monthly rainfall prediction.

scholarly journals A Genetic Algorithm Based Support Vector Machine Model for Blood-Brain Barrier Penetration Prediction

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Daqing Zhang ◽  
Jianfeng Xiao ◽  
Nannan Zhou ◽  
Mingyue Zheng ◽  
Xiaomin Luo ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Blood Brain Barrier ◽  
Subset Selection ◽  
Feature Subset Selection ◽  
Brain Barrier ◽  
Support Vector ◽  
Feature Subset ◽  
Svm Model ◽  
Kernel Parameters

Blood-brain barrier (BBB) is a highly complex physical barrier determining what substances are allowed to enter the brain. Support vector machine (SVM) is a kernel-based machine learning method that is widely used in QSAR study. For a successful SVM model, the kernel parameters for SVM and feature subset selection are the most important factors affecting prediction accuracy. In most studies, they are treated as two independent problems, but it has been proven that they could affect each other. We designed and implemented genetic algorithm (GA) to optimize kernel parameters and feature subset selection for SVM regression and applied it to the BBB penetration prediction. The results show that our GA/SVM model is more accurate than other currently available logBBmodels. Therefore, to optimize both SVM parameters and feature subset simultaneously with genetic algorithm is a better approach than other methods that treat the two problems separately. Analysis of our logBBmodel suggests that carboxylic acid group, polar surface area (PSA)/hydrogen-bonding ability, lipophilicity, and molecular charge play important role in BBB penetration. Among those properties relevant to BBB penetration, lipophilicity could enhance the BBB penetration while all the others are negatively correlated with BBB penetration.

A New Hybrid Feature Subset Selection Framework Based on Binary Genetic Algorithm and Information Theory

2019 ◽  
Vol 18 (03) ◽  
pp. 1950020 ◽  
Author(s):  
Alok Kumar Shukla ◽  
Pradeep Singh ◽  
Manu Vardhan
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Classification Accuracy ◽  
B Cell Lymphoma ◽  
Feature Subset Selection ◽  
Classification Model ◽  
Significant Feature ◽  
Support Vector ◽  
Feature Subset ◽  
Binary Genetic Algorithm

The explosion of the high-dimensional dataset in the scientific repository has been encouraging interdisciplinary research on data mining, pattern recognition and bioinformatics. The fundamental problem of the individual Feature Selection (FS) method is extracting informative features for classification model and to seek for the malignant disease at low computational cost. In addition, existing FS approaches overlook the fact that for a given cardinality, there can be several subsets with similar information. This paper introduces a novel hybrid FS algorithm, called Filter-Wrapper Feature Selection (FWFS) for a classification problem and also addresses the limitations of existing methods. In the proposed model, the front-end filter ranking method as Conditional Mutual Information Maximization (CMIM) selects the high ranked feature subset while the succeeding method as Binary Genetic Algorithm (BGA) accelerates the search in identifying the significant feature subsets. One of the merits of the proposed method is that, unlike an exhaustive method, it speeds up the FS procedure without lancing of classification accuracy on reduced dataset when a learning model is applied to the selected subsets of features. The efficacy of the proposed (FWFS) method is examined by Naive Bayes (NB) classifier which works as a fitness function. The effectiveness of the selected feature subset is evaluated using numerous classifiers on five biological datasets and five UCI datasets of a varied dimensionality and number of instances. The experimental results emphasize that the proposed method provides additional support to the significant reduction of the features and outperforms the existing methods. For microarray data-sets, we found the lowest classification accuracy is 61.24% on SRBCT dataset and highest accuracy is 99.32% on Diffuse large B-cell lymphoma (DLBCL). In UCI datasets, the lowest classification accuracy is 40.04% on the Lymphography using k-nearest neighbor (k-NN) and highest classification accuracy is 99.05% on the ionosphere using support vector machine (SVM).

scholarly journals On-line Signature Verification Based on GA-SVM

2015 ◽  
Vol 11 (6) ◽  
pp. 49 ◽  
Author(s):  
Dong Huang ◽  
Jian Gao
Keyword(s):  
Genetic Algorithm ◽  
Feature Subset Selection ◽  
Signature Verification ◽  
Support Vector ◽  
Svm Classifier ◽  
Support Vector Data Description ◽  
Feature Subset ◽  
Dynamic Features ◽  
On Line ◽  
One Class Classifier

With the development of pen-based mobile device, on-line signature verification is gradually becoming a kind of important biometrics verification. This thesis proposes a method of verification of on-line handwritten signatures using both Support Vector Data Description (SVM) and Genetic Algorithm (GA). A 27-parameter feature set including shape and dynamic features is extracted from the on-line signatures data. The genuine signatures of each subject are treated as target data to train the SVM classifier. As a kernel based one-class classifier, SVM can accurately describe the feature distribution of the genuine signatures and detect the forgeries. To improving the performance of the authentication method, genetic algorithm (GA) is used to optimise classifier parameters and feature subset selection. Signature data form the SVC2013 database is used to carry out verification experiments. The proposed method can achieve an average Equal Error Rate (EER) of 4.93% of the skill forgery database.

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