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  • 标题:Evolutionary deep belief networks with bootstrap sampling for imbalanced class datasets
  • 本地全文:下载
  • 作者:A’inur A’fifah Amri ; Amelia Ritahani Ismail ; Omar Abdelaziz Mohammad
  • 期刊名称:IJAIN (International Journal of Advances in Intelligent Informatics)
  • 印刷版ISSN:2442-6571
  • 电子版ISSN:2548-3161
  • 出版年度:2019
  • 卷号:5
  • 期号:2
  • 页码:123-136
  • DOI:10.26555/ijain.v5i2.350
  • 语种:English
  • 出版社:Universitas Ahmad Dahlan
  • 摘要:Imbalanced class data is a common issue faced in classification tasks. Deep Belief Networks (DBN) is a promising deep learning algorithm when learning from complex feature input. However, when handling imbalanced class data, DBN encounters low performance as other machine learning algorithms. In this paper, the genetic algorithm (GA) and bootstrap sampling are incorporated into DBN to lessen the drawbacks occurs when imbalanced class datasets are used. The performance of the proposed algorithm is compared with DBN and is evaluated using performance metrics. The results showed that there is an improvement in performance when Evolutionary DBN with bootstrap sampling is used to handle imbalanced class datasets.
  • 关键词:Imbalanced class;Deep belief networks;Genetic algorithm;Bootstrapping sampling;Complex feature input
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