Binary tree-based SVM (support vector machine) classification method

Abstract

The invention discloses a binary tree-based SVM (support vector machine) classification method. The binary tree-based SVM classification method comprises the following steps: 1, acquiring signals, namely detecting working state information of an object to be detected in N different working states through a state information detection unit, synchronously transmitting the detected signals to a data processor, and acquiring N groups of working state detection information which corresponds to the N different working states; 2, extracting characteristics; 3, acquiring training samples, namely randomly extracting m detections signals to form training sample sets respectively from the N groups of working state detection information which are subjected to the characteristic extraction; 4, determining classification priority; 5, establishing a plurality of classification models; 6 training a plurality of classification models; and 7, acquiring signals in real time and synchronously classifying. The binary tree-based SVM classification method is reasonable in design, easy to operate, convenient to implement, good in use effect and high in practical value; and optimal parameters of an SVM classifier can be chosen, influence on the classification due to noises and isolated points can be reduced, and classification speed and precision are improved.

Description

technical field [0001] The invention belongs to the technical field of defect recognition, in particular to a classification method based on a binary tree support vector machine. Background technique [0002] The intelligent recognition of defects is to determine the recognition algorithm based on the defect feature extraction, design the corresponding classifier, and use the sample set for training, and finally complete the automatic classification of defects. At present, there are mainly statistical classification methods, rule-based classification methods and learning-based classification methods. Common classifiers include decision tree classification, Bayesian classification, fuzzy classification, artificial neural network classification and support vector machine classification, etc. The latter two are more widely used in data signal processing. Among them, the artificial neural network classification method is often difficult to meet the conditions in practical appli...

AI Technical Summary

Problems solved by technology

These methods have their own advantages and disadvantages in multi-classification. Studies have shown that: 1-a-r classification method is simple and effective, and can be used for large-scale data, but when the working set is too large, the training speed will be very slow; at the same time, it has misclassification, Rejected areas, poor generalization ability

The 1-a-1 classification speed is faster than the traditional 1-a-r method, and its classification accuracy is also higher than 1-a-r; but its disadvantage is: if a single two-class classifier is not standardized, the entire classifier will tend to be too high. Learning, the number of classifiers increases sharply with the number of classes, resulting in very slow decision-making speed, unbounded promotion error and misclassification and rejection areas

DTSVM and HSVM adopt the combination strategy of tree structure, which has high training and classification speed, but the classification tree has accumulation of misclassification. If a reasonable tree structure is selected, higher classification speed and accuracy can be obtained.

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