Abstract
According to the traditional classification method of network capital resources, there are some problems such as low efficiency, low recall rate, and low precision rate of information. Therefore, this paper proposes a new classification method of network capital resources based on SVM algorithm. Firstly, the original sample data are analyzed by principal component analysis to realize the design of resource classification process. Then, the dimension reduction of network resources data is realized by word segmentation and denoising. Thirdly, the reduced sample data are trained by the SVM classifier, and the best parameters of the reduced data are obtained by the grid search method. Lastly, the search range of SVM classifier parameters based on the original sample data is reset, so as to quickly obtain the best SVM classifier parameters of the original sample data and realize the classification. The experimental results show that this method can improve the recall and precision of network resource information and shorten the classification time of network resources.
Highlights
Introduction of theSupport Vector Machine (SVM) algorithm to resolve classification method of network capital resources
In order to solve the above problems, this paper proposes a network resource classification method based on modified SVM algorithm. is method does not involve probability measure and law of large numbers, so it is different from the existing statistical methods
The reduced sample data are trained by the SVM classifier, and the best parameters of the reduced data are obtained by the grid search method
Summary
In order to solve the problem that the time for grid search method to find the best parameters increases exponentially due to the large amount of sample data and multidimension of object-oriented network resource classification, this paper first uses PCA to reduce the dimension of data and eliminate the correlation between sample attributes and sets the initial value of the parameter search range based on the reduced dimension data. E new samples are trained based on SVM algorithm, and the best parameters of PCA dimension reduction data are obtained by the grid search method. Assuming that the optimal penalty factor and kernel parameter of SVM classifier based on PCA dimension reduction data are, respectively, cPCA and cPCA, they are Original sample data. Based on the new grid search range, the optimal parameter combination c′, c′ is obtained by the grid search method for the original sample data. The data acquisition terminal is used to collect the data, and the data processing center is used to classify the collected data. ere are many data acquisition terminals in this system
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