SVM (support vector machine) multi-class classification method based on inter-class separability and adopting complete binary tree

Abstract

The invention discloses an SVM (support vector machine) multi-class classification method based on inter-class separability and adopting a complete binary tree. The method comprises the following steps: the inter-class separability measure is calculated according to a sample distribution variance and an inter-class centre distance; two classes with minimum separability measure are selected from a class set and placed into a left child node set; a class having the maximum sum with the separability measure of all the classes in the left child node set is placed into a right child node set, and then a class having the minimum sum with the separability measure of all the classes in the left child node set is placed into the right child node set; classes are selected and added to another child node set; the process is repeated until the class set is divided completely; the left child node set and the right child node set are divided until each child node set only comprises one class, the complete binary tree is constructed, and SVM classifiers are trained for classification. By means of the method, the complete binary tree can be constructed and the classification accuracy can be improved.

Description

technical field [0001] The invention belongs to the technical field of support vector machines, and more specifically relates to a complete binary tree SVM multi-classification method based on class separability. Background technique [0002] SVM (Support Vector Machine, Support Vector Machine) is a machine learning method for small sample training and classification. SVM has shown good generalization performance in pattern classification problems, and is very suitable for solving high-dimensional pattern recognition, small sample and nonlinear problem. The traditional SVM was originally designed to solve two-class classification problems, and there will be problems when it is directly applied to multi-class classification problems; and most of the practical applications are multi-classification problems, how to extend the two-classification idea to multi-classification and Maintaining its excellent classification performance has important practical significance, and it is ...

AI Technical Summary

Problems solved by technology

The disadvantage of this multi-classification method is that the structure of the decision binary tree will have a great impact on the performance of the SVM classifier, and the classification performance of different decision tree structures is different.

In classification problems, the Euclidean distance between sample class centers is often used to measure the separability between classes, but the Euclidean distance between classes cannot represent the separability of sample classes well, and it needs to be improved

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