Loan Prediction Model Based on AdaBoost and PSO-SVM

DOI

10.2991/ncce-18.2018.120How to use a DOI?

Keywords

Integrated learning; SVM; PSO; AdaBoost; loan prediction model.

Abstract

In view of the problem that the slow manual approval of loans and traditional classification algorithms have low recognition rate for a few sample classes, an integrated learning classification model based on PSO optimization support vector machines is proposed. Particle swarm optimization (PSO) is used to optimize the model parameters of SVM classifier. The AdaBoost integrated learning method is used to integrate SVM weak classifiers, and a loan prediction model based on multi-classifier optimization and integration is established. Taking the Lending Clud loan data set as the research object, the loan prediction model was built. The simulation results show that compared with the standard SVM algorithm and the PSO-SVM algorithm, the AdaBoost-PSO-SVM method can effectively improve the accuracy of the classification of a small number of samples, The classification accuracy of the whole sample and the generalization rate, the accuracy of the model applied to loan prediction is obviously better than other models.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Tao Zhang
AU  - Baodian Li
PY  - 2018/05
DA  - 2018/05
TI  - Loan Prediction Model Based on AdaBoost and PSO-SVM
BT  - Proceedings of the 2018 International Conference on Network, Communication, Computer Engineering (NCCE 2018)
PB  - Atlantis Press
SP  - 733
EP  - 739
SN  - 1951-6851
UR  - https://doi.org/10.2991/ncce-18.2018.120
DO  - 10.2991/ncce-18.2018.120
ID  - Zhang2018/05
ER  -