A support vector machine integrated learning method based on AdaBoost

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...

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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

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