首页> 外文期刊>Knowledge-Based Systems >Output-based transfer learning in genetic programming for document classification
【24h】

Output-based transfer learning in genetic programming for document classification

机译:文档分类遗传编程基于输出的转移学习

获取原文
获取原文并翻译 | 示例

摘要

Transfer learning has been studied in document classification for transferring a model trained from a source domain (SD) to a relatively similar target domain (TD). In feature-based transfer learning techniques, there is an investigation on the features being transferred from SD to TD. This paper conducts an investigation on an output-based transfer learning system using Genetic Programming (GP) in document classification tasks, which automatically selects features to construct classifiers. The proposed GP system directly generates programs from a set of sparse features and only considers the output change of the evolved programs from SD to TD. A linear model is then used to combine existing GP programs from SD as features to TD. Also, new GP programs are mutated from the programs evolved in SD to improve the accuracy. Via directly utilizing the evolved GP programs and their mutations, the feature extraction and estimation processes on TD are avoided. The results for the experiments demonstrates that the GP programs from SD can be effectively used for classifying documents in the relevant TD. The results also show that it is easy to train effective classifiers on TD when the GP programs are used as features. Furthermore, the proposed linear model, using multiple GP programs from SD as its inputs, outperforms single GP programs which are directly obtained from TD. (C) 2020 Elsevier B.V. All rights reserved.
机译:已经在文档分类中研究了转移学习,用于将从源域(SD)训练的模型传输到相对相似的目标域(TD)。在基于特征的转移学习技术中,对从SD转移到TD的特征有研究。本文在文档分类任务中对使用基于输出的转移学习系统进行了调查,它在文档分类任务中自动选择要构造分类器的功能。所提出的GP系统直接从一组稀疏功能生成程序,并仅考虑从SD到TD的进化程序的输出变化。然后使用线性模型将来自SD的现有GP程序与TD相结合。此外,新的GP程序是从SD中演进的程序变异,以提高准确性。通过直接利用进化的GP程序及其突变,避免了TD的特征提取和估计过程。实验结果表明,来自SD的GP程序可以有效地用于在相关TD中进行分类文件。结果还表明,当GP程序用作特征时,易于培训TD上的有效分类器。此外,所提出的线性模型,使用来自SD的多个GP程序作为其输入,优于直接从TD获得的单个GP程序。 (c)2020 Elsevier B.v.保留所有权利。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号