Prediction method for unbalanced data set based on isolated forest learning

Abstract

The invention discloses a prediction method for an unbalanced data set based on isolated forest learning. The prediction method comprises the following steps: receiving a prediction request; collecting data, and defining features and labels in the data set and the number of minority class samples and majority class samples; converting a non-numerical feature column and a label column in the data set into classification numerical values; synthesizing minority class samples by using a majority class weighted minority class oversampling technology to form a balance data set; performing abnormal point identification and removal on the balance data set by using an isolated forest algorithm; then performing data standardization, and dividing a training set and a test set; constructing and training a support vector machine classifier model by using the training set; adjusting hyper-parameters of the support vector machine classifier model through a genetic algorithm, and obtaining a prediction model after training is completed; and inputting the test set into the prediction model to obtain a prediction result. The prediction method for the unbalanced data set based on isolated forest learning has the characteristics of stable prediction result and high prediction precision.

Description

technical field [0001] The invention belongs to the technical field of prediction methods for category unbalanced data sets, and in particular relates to a prediction method for unbalanced data sets based on isolated forest learning. Background technique [0002] With the rapid development of sensor technology, computer technology, communication technology, data storage and other technologies, the Internet, process industry and other fields generate and store a large amount of data. Machine learning is a mainstream intelligent data processing technology, and classification algorithm is one of the key technologies of machine learning. It can use big data to build a classification model with strong generalization ability and extract useful information in the data. focus on. Traditional classification methods usually assume that each category in the data set contains the same number of samples and the cost of misclassification is equal. However, data in the real world often h...

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Problems solved by technology

[0004] The purpose of the present invention is to provide a prediction method for unbalanced data sets based on isolated forest learning, which solves the problem of sample overlap and abnormal points that make prediction results unstable and low in accuracy when generating minority samples in existing methods question

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