Improved Random Balanced Sampling Bagging Algorithm for Network Loan Research

doi:10.3969/j.issn.1006-2475.2019.04.003

Abstract

Abstract: The data of network loan users in Internet finance has the characteristics of class imbalance, which seriously affects the performance of traditional classifiers. The random balanced sampling algorithm considers all samples equally in the process of resampling the original data set. In this paper, the performance of the sample points is fully considered in the process of balanced sampling, and it is divided into three types of samples: safe, boundary, and noisy. The corresponding sampling method is adopted for different types of samples to obtain a balanced new data set, and then the Bagging integration of the data set is performed to improve the generalization performance of the algorithm. The results show that the Improved Random Balanced Sampling(IRBS) Bagging algorithm in this paper can better classify loan users.

Key words: category imbalance, random balanced sampling, Bagging integration

CLC Number:

TP391

GUO Bing-nan， WU Guang-chao . Improved Random Balanced Sampling Bagging Algorithm for Network Loan Research[J]. Computer and Modernization, 2019, 0(04): 11-.

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