Semi-supervised learning-based twinborn extreme learning machine classification data processing method

Abstract

The invention relates to a semi-supervised learning-based twinborn extreme learning machine classification data processing method, and belongs to the technical field of data mining and processing. According to the method, a semi-supervised learning algorithm for carrying out classification by adoption of two non-parallel classification surfaces on the basis of a random feature mapping mechanism isused for combining technologies of popular regularization, random feature mapping and the two non-parallel classification surfaces, so that defects, on problems of cross data and the like, of singleclassification surfaces are solved, relatively strong robustness is ensured when singular points exist, and the problem that the past algorithms cannot satisfy the generalization ability and the calculation efficiency requirement on few labeled samples at the same time is solved. The method is capable of sufficiently mining information in unlabeled data under the condition that less labeled data exists, is particularly suitable for fault diagnosis in the newly-developing technical field of high-speed rails, draught fans and the like, is high in calculation speed, is basically capable of carrying out real-time judgement, and is high in classification correctness.

Description

technical field [0001] The invention relates to a data processing method for twin extreme learning machine classification based on semi-supervised learning, and belongs to the technical field of data mining and processing. Background technique [0002] In recent years, with the improvement of information collection technology and computer storage technology, in order to achieve the goal of informatization and intelligent management and operation, enterprises have accumulated a large amount of data information in various stages of enterprise operation, such as status information of high-speed rail, wind turbine operation, Fault information, etc., these information can be used as samples for machine learning, and the labeled data is called labeled data, and these labeled data are learned by machine learning algorithms, and the corresponding relationship between the fault phenomenon and the machine status information can be found or However, in the fault diagnosis of the entire...

AI Technical Summary

Problems solved by technology

The algorithm combines the techniques of popular regularization, random feature mapping and two non-parallel classification surfaces to solve the defects of single classification surface on cross data and other problems, and can guarantee strong robustness in the case of singular points. At the same time, it overcomes the problem that existing algorithms cannot meet the requirements of generalization ability and computational efficiency on a small number of labeled samples.

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