An MIMLSVM algorithm based on ECC

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• 作者：Cunhe Li (1)
  Yanli Zhang (1)
  Lei Lu (1)
  
  1. College of Computer and Communication Engineering ; China University of Petroleum ; Qingdao ; 266580 ; China
  
• 关键词：Support vector machine ; Multi ; instance multi ; label ; Classifier chains ; Label correlations
• 刊名：Applied Intelligence
• 出版年：2015
• 出版时间：April 2015
• 年：2015
• 卷：42
• 期：3
• 页码：537-543
• 全文大小：510 KB
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• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Mechanical Engineering
  Manufacturing, Machines and Tools
  
• 出版者：Springer Netherlands
• ISSN：1573-7497

文摘

In the multi-instance multi-label learning framework, an example is described by multiple instances and associated with multiple class labels at the same time. An idea of tackling with multi-instance multi-label problems is to identify its equivalence in the traditional supervised learning framework. However, some useful information such as the correlation between labels may be lost in the process of degeneration, which will influence the classification performance. In E-MIMLSVM+ algorithm, multi-task learning techniques are utilized to incorporate label correlations, while it is time consuming as well as memory consuming. Therefore, we propose an improved algorithm. In our algorithm, the classifier chains method is applied in E-MIMLSVM+ to incorporate label correlations instead of multi-task learning techniques. The experimental results show that the proposed algorithm can reduce time complexity and improve the predictive performance.