Signal random forest classification method, system and device based on decision tree accuracy and correlation measurement

Abstract

The invention discloses a signal random forest classification method, system and device based on decision tree accuracy and correlation measurement, and belongs to the field of signal classification and recognition. The objective of the invention is to solve the problem of low classification accuracy of a single decision tree classifier in a traditional random forest classifier. The method comprises the following steps: firstly, establishing decision trees, verifying each decision tree by using three groups of reserved data sets, calculating the accuracy rate of the i-th decision tree, and sorting all the decision trees in a descending order according to the classification accuracy rate; for the determined data set, adopting a vector inner product method to calculate and store an inner product numerical value between the decision trees, reserving the decision trees of which the vector inner products are smaller than or equal to an inner product threshold value, and otherwise, marking the decision trees with low classification accuracy in each pair of decision trees of which the vector inner products are calculated as deletable; sequentially deleting the decision trees marked as deletable according to the classification accuracy from low to high until the number of the remaining decision trees is N; and performing voting by adopting the final classifier to determine a final classification result. The method, system and device are mainly used for signal classification and identification.

Description

technical field [0001] The invention belongs to the field of signal classification and recognition, and in particular relates to a signal classification and recognition method, system and device for electronic equipment. Background technique [0002] In the field of electronic equipment detection technology, signal detection is often performed on sealed electronic equipment / sealed electronic components, but the current signal detection methods have problems such as low accuracy, especially for the detection and identification of similar redundant signals and component signals. The excess signal is the sound signal generated by the vibration after the free excess particles collide with the inner wall of the sealed device when the sealed relay is excited by the outside; the component signal is the vibration signal generated by the intrinsically loose components of the relay after the vibration is applied. The waveforms output by the component signal and the redundant signal th...

AI Technical Summary

Problems solved by technology

[0008] The main purpose of the present invention is to solve the problem of low classification accuracy of a single decision tree classifier in traditional random forest classifiers, especially the problem of low classification accuracy for signal detection of electronic equipment

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