An Improved BP Neural Network Algorithm for Text Classification

被引：0

作者：

Lei, Fei ^{[1
]}

Yu, Yongbin ^{[1
]}

Guo, Yuxin ^{[1
]}

Tashi, Nyima ^{[2
]}

Zhang, Huan ^{[1
]}

Dang, Bo ^{[1
]}

机构：

[1] Univ Elect Sci & Technol China, Sch Informat & Software Engineer, Chengdu, Sichuan, Peoples R China

[2] Tibet Univ Lhasa, Sch Informat Sci & Technol, Tibet, Peoples R China

来源：

2018 37TH CHINESE CONTROL CONFERENCE (CCC) | 2018年

基金：

中国国家自然科学基金;

关键词：

text classification; BPNN; feature selection; initial weights; genetic algorithm;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper presents an improved back propagation neural network (BPNN), which highlights a novel feature selection metrics and the combination of Genetic Algorithm (GA) and BPNN. On one hand, feature selection metrics combined deflection speed and square error (DSSE) is designed to reduce dimensionality and optimize the weights of BPNN, which is capable of decreasing the training time. On the other hand, GA is introduced to optimize the hidden layer of BPNN. Experiment results demonstrate that our proposed algorithm reduces training time of 8% and improves the accuracy of prediction of 2.5%.

引用

页码：4474 / 4478

页数：5

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