Self-paced Learning for Imbalanced Data

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• 关键词：Self ; paced learning ; Cost ; sensitive learning ; Imbalanced data
• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：9621
• 期：1
• 页码：564-573
• 全文大小：199 KB
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• 作者单位：Maciej Zięba (17)
  Jakub M. Tomczak (17)
  Jerzy Świątek (17)
  
  17. Department of Computer Science, Wroclaw University of Technology, Wybrzeze Wyspianskiego 27, 50-370, Wroclaw, Poland
  
• 丛书名：Intelligent Information and Database Systems
• ISBN：978-3-662-49381-6
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349

文摘

In this paper, we propose a novel training paradigm that combines two learning strategies: cost-sensitive and self-paced learning. This learning approach can be applied to the decision problems where highly imbalanced data is used during training process. The main idea behind the proposed method is to start the learning process by taking large number of minority examples and only the easiest majority objects and then gradually turning to more difficult cases. We examine the quality of this training paradigm comparing to other learning schemas for neural network model using a set of highly imbalanced benchmark datasets.