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A support vector machine integrated learning method based on AdaBoost

A technology of support vector machine and ensemble learning, applied in the field of data mining and machine learning, which can solve problems such as low precision

Inactive Publication Date: 2019-03-15
CHINA UNIV OF PETROLEUM (EAST CHINA)
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Problems solved by technology

[0003] The purpose of the present invention is to provide a support vector machine integrated learning method based on AdaBoost, and use W-SVM to construct a weak classifier for the existing support vector machine learning method that has low accuracy when dealing with class imbalance classification problems. , and integrate the weak classifier into a strong classifier based on the AdaBoost algorithm framework

Method used

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  • A support vector machine integrated learning method based on AdaBoost
  • A support vector machine integrated learning method based on AdaBoost
  • A support vector machine integrated learning method based on AdaBoost

Examples

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Embodiment 1

[0028] Embodiment 1: Take the state prediction of blast furnace temperature ([Si]) of Laiwu Iron & Steel No. 1 blast furnace (BF(a)) and Baotou No. 7 blast furnace (BF(b)) as an example. figure 2 The time series of blast furnace temperature [Si] and blast furnace air volume are given. Depend on figure 2 It can be seen that there are significant differences in scale between [Si] and air volume. Large-scale variables will cover up the impact of small-scale variables on the model, which will seriously affect the prediction accuracy of the model. To do this, first use the formula Normalize the sampled data so that the input variables are all on the same scale. Determine the training sample set and the test sample set, and conduct cluster analysis on the furnace temperature [Si] through the K-means algorithm on the training sample set, and divide the furnace temperature [Si] into three states: low temperature, high temperature and normal state. The low temperature and high t...

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Abstract

The invention relates to a support vector machine integrated learning method based on AdaBoost. In order to overcome the defect that an existing support vector machine learning method is low in precision when processing a class imbalance classification problem, the invention provides a support vector machine integrated learning method based on AdaBoost, and a weighted support vector machine (W-SVM) is used. According to the method, sample distribution information can be deeply mined, prediction precision can be remarkably improved, and the method is an effective tool for solving the class imbalance problem.

Description

technical field [0001] The invention belongs to the field of data mining and machine learning, and relates to data mining and data processing methods, in particular to an AdaBoost-based support vector machine integrated learning method. Background technique [0002] Support vector machine is a typical kernel learning model established on the basis of the principle of structural risk minimization. It is the most commonly used supervised learning algorithm. Its basic idea is to map the training data to a high-dimensional Hilbert feature space through nonlinear mapping. Subsequently, a maximum margin classification hyperplane is constructed in the Hilbert space and linear classification is performed. However, the classifier obtained by training a single support vector machine often has many shortcomings such as low prediction accuracy when dealing with complex problems. In order to improve the practical application effect, under the basic framework of ensemble learning, we pro...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06N20/10G06N20/20
CPCG06F18/285G06F18/2411
Inventor 陈宏义雷鹤杰梁锡军渐令
Owner CHINA UNIV OF PETROLEUM (EAST CHINA)
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