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A multi-label learning based kernel automatic recommendation method for support vector machine.

Zhang X, Song Q - PLoS ONE (2015)

Bottom Line: Considering the tradeoff between classification success ratio and CPU time, there may be multiple kernel functions performing equally well on the same classification problem.For each data set, the meta-knowledge data base is first created by extracting the feature vector of data characteristics and identifying the corresponding applicable kernel set.Finally, the appropriate kernel functions are recommended to a new data set by the recommendation model according to the characteristics of the new data set.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science & Technology, Xi'an Jiaotong University, 28 Xian-Ning West Road, Xi'an, Shaanxi 710049, P. R. China.

ABSTRACT
Choosing an appropriate kernel is very important and critical when classifying a new problem with Support Vector Machine. So far, more attention has been paid on constructing new kernels and choosing suitable parameter values for a specific kernel function, but less on kernel selection. Furthermore, most of current kernel selection methods focus on seeking a best kernel with the highest classification accuracy via cross-validation, they are time consuming and ignore the differences among the number of support vectors and the CPU time of SVM with different kernels. Considering the tradeoff between classification success ratio and CPU time, there may be multiple kernel functions performing equally well on the same classification problem. Aiming to automatically select those appropriate kernel functions for a given data set, we propose a multi-label learning based kernel recommendation method built on the data characteristics. For each data set, the meta-knowledge data base is first created by extracting the feature vector of data characteristics and identifying the corresponding applicable kernel set. Then the kernel recommendation model is constructed on the generated meta-knowledge data base with the multi-label classification method. Finally, the appropriate kernel functions are recommended to a new data set by the recommendation model according to the characteristics of the new data set. Extensive experiments over 132 UCI benchmark data sets, with five different types of data set characteristics, eleven typical kernels (Linear, Polynomial, Radial Basis Function, Sigmoidal function, Laplace, Multiquadric, Rational Quadratic, Spherical, Spline, Wave and Circular), and five multi-label classification methods demonstrate that, compared with the existing kernel selection methods and the most widely used RBF kernel function, SVM with the kernel function recommended by our proposed method achieved the highest classification performance.

No MeSH data available.


Comparison of different multi-label classification methods in terms of HR, Precision and ARR.
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pone.0120455.g006: Comparison of different multi-label classification methods in terms of HR, Precision and ARR.

Mentions: In this subsection, we analyze the effect of five multi-label classification methods (BR, LP, CLR, ILC and ML-KNN (k = 5)) and three representative feature selection methods (Relief, CHI and IG) on the recommendations for the 132 data sets in terms of the average HR, Precision and ARR for three different β values. Figs 6 and 7 illustrates the sensitivity analysis results of multi-label classification methods and feature selection methods, respectively.


A multi-label learning based kernel automatic recommendation method for support vector machine.

Zhang X, Song Q - PLoS ONE (2015)

Comparison of different multi-label classification methods in terms of HR, Precision and ARR.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4403820&req=5

pone.0120455.g006: Comparison of different multi-label classification methods in terms of HR, Precision and ARR.
Mentions: In this subsection, we analyze the effect of five multi-label classification methods (BR, LP, CLR, ILC and ML-KNN (k = 5)) and three representative feature selection methods (Relief, CHI and IG) on the recommendations for the 132 data sets in terms of the average HR, Precision and ARR for three different β values. Figs 6 and 7 illustrates the sensitivity analysis results of multi-label classification methods and feature selection methods, respectively.

Bottom Line: Considering the tradeoff between classification success ratio and CPU time, there may be multiple kernel functions performing equally well on the same classification problem.For each data set, the meta-knowledge data base is first created by extracting the feature vector of data characteristics and identifying the corresponding applicable kernel set.Finally, the appropriate kernel functions are recommended to a new data set by the recommendation model according to the characteristics of the new data set.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science & Technology, Xi'an Jiaotong University, 28 Xian-Ning West Road, Xi'an, Shaanxi 710049, P. R. China.

ABSTRACT
Choosing an appropriate kernel is very important and critical when classifying a new problem with Support Vector Machine. So far, more attention has been paid on constructing new kernels and choosing suitable parameter values for a specific kernel function, but less on kernel selection. Furthermore, most of current kernel selection methods focus on seeking a best kernel with the highest classification accuracy via cross-validation, they are time consuming and ignore the differences among the number of support vectors and the CPU time of SVM with different kernels. Considering the tradeoff between classification success ratio and CPU time, there may be multiple kernel functions performing equally well on the same classification problem. Aiming to automatically select those appropriate kernel functions for a given data set, we propose a multi-label learning based kernel recommendation method built on the data characteristics. For each data set, the meta-knowledge data base is first created by extracting the feature vector of data characteristics and identifying the corresponding applicable kernel set. Then the kernel recommendation model is constructed on the generated meta-knowledge data base with the multi-label classification method. Finally, the appropriate kernel functions are recommended to a new data set by the recommendation model according to the characteristics of the new data set. Extensive experiments over 132 UCI benchmark data sets, with five different types of data set characteristics, eleven typical kernels (Linear, Polynomial, Radial Basis Function, Sigmoidal function, Laplace, Multiquadric, Rational Quadratic, Spherical, Spline, Wave and Circular), and five multi-label classification methods demonstrate that, compared with the existing kernel selection methods and the most widely used RBF kernel function, SVM with the kernel function recommended by our proposed method achieved the highest classification performance.

No MeSH data available.