Unbalanced data sampling method in improved C4.5 decision tree algorithm

Abstract

The invention relates to an unbalanced data sampling method in an improved C4.5 decision tree algorithm. The method comprises the steps as follows: firstly, initial weights of various samples are determined according to the number of various samples; the weights of the samples are modified through the training result of the improved C4.5 decision tree algorithm in each round; the information gain ratio and misclassified sample weights are taken into account by a division standard of the improved C4.5 algorithm; the final weights of the samples are obtained after T iterations; the samples in minority class boundary regions and majority class center regions are found out according to the sample weights; over-sampling is carried out on the samples in the minority class boundary regions by an SMOTE algorithm; and under-sampling is carried out on majority class samples by a weight sampling method, so that the samples in the center regions are relatively easily selected to improve the balance degree of different classes of data, and the recognition rates of the minority class and the overall data set are improved. According to the unbalanced data sampling method in the improved C4.5 decision tree algorithm, weight modification is carried out through the improved C4.5 decision tree algorithm; and over-sampling and under-sampling are specifically carried out according to the sample weights, so that the phenomena of classifier over-fitting, loss of useful information of the majority class and the like are effectively avoided.

Description

technical field [0001] The invention belongs to the technical field of data processing and relates to an unbalanced data sampling method under an improved C4.5 decision tree algorithm. Background technique [0002] An unbalanced data set means that in the data set, the number of samples of a certain class is far less than the number of samples of other classes, and the class with the majority is called the majority class, while the class with the minority is called the minority class. The classification problems of unbalanced datasets exist in a large number of people's real life and industrial production, such as customer churn prediction, DNA microarray data analysis, software defect prediction, spam filtering, text classification, medical diagnosis, etc., in these applications , the minority class classification accuracy is often more important. Therefore, improving the classification accuracy of the minority class becomes a research focus in imbalanced datasets. [000...

AI Technical Summary

Problems solved by technology

The commonly used strategies are: cost-sensitive method, which introduces cost-sensitive factors on the basis of traditional classification algorithms, and designs cost-sensitive classification algorithms, such as cost-sensitive decision trees, cost-sensitive support vector machines, etc. The key to these algorithms lies in the error However, in most cases, the real misclassification cost is difficult to be accurately estimated

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