Subway fault data classification method based on unbalanced data set

Abstract

The invention discloses a subway fault data classification method based on an unbalanced data set. The method comprises the following steps: inputting an original unbalanced data set, and dividing theunbalanced data set into a training data set and a test data set; the training data set is divided into a positive class sample set and a negative class sample set, wherein the positive class sampleset is a minority class sample, and the negative class sample set is a majority class sample; dividing the positive class sample set into K different clusters by using a K-Means clustering algorithm;for each cluster, sampling the data set by using an improved SMOTE algorithm to finally obtain a balanced data set; taking the SVM as a weak classifier, and constructing an integrated classifier by using an AdaBoost algorithm; and evaluating the performance of the integrated classifier by using the test data set. The method can effectively improve the recognition rate of a small number of types ofsamples in the unbalanced data set while guaranteeing the overall accuracy, and has a better effect in the classification prediction of the unbalanced data set.

Description

technical field [0001] The invention belongs to the technical field of data mining, in particular to a subway fault data classification method based on an unbalanced data set. Background technique [0002] During the long-term operation of the subway, the probability of equipment failure is very high. If it cannot be dealt with in time, it will cause great losses. Therefore, timely and effective fault diagnosis of the subway is becoming increasingly important. In fault diagnosis, fault data classification is the key technology. Classification methods are widely used in the field of prediction, and most classification methods require that the distribution of data is relatively uniform. If the distribution of the data is seriously unbalanced, the minority data is likely to be treated as noise. Data in real life often presents the characteristics of unbalanced distribution, that is, in the data set, the number of samples of different categories varies greatly. A large number...

AI Technical Summary

Problems solved by technology

The disadvantage of this method is that undersampling technology can easily delete samples containing important information while deleting samples.

[0006] The traditional SMOTE algorithm does not consider the spatial distribution of samples enough, and lacks judgment rules for synthetic samples, which leads to the intrusion of synthetic positive samples into the distribution space of negative samples and affects the data classification effect.

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