Evolving personalized modeling system for integrated feature, neighborhood and parameter optimization utilizing gravitational search algorithm

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• 作者：Wen Liang (1)
  Yingjie Hu (1)
  Nikola Kasabov (1)
  
  1. Knowledge Engineering and Discovery Research Institute ; Auckland University of Technology ; Auckland ; New Zealand
  
• 关键词：Personalized modeling ; Feature selection ; Neighborhood optimization ; Parameter optimization ; Gravitational search algorithm (GSA)
• 刊名：Evolving Systems
• 出版年：2015
• 出版时间：March 2015
• 年：2015
• 卷：6
• 期：1
• 页码：1-14
• 全文大小：965 KB
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• 刊物类别：Engineering
• 刊物主题：Artificial Intelligence and Robotics
  Complexity
  Statistical Physics, Dynamical Systems and Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1868-6486

文摘

This paper introduces a new evolving personalized modeling method and system (evoPM) that integrates gravitational search inspired algorithm (GSA) for selecting informative features, optimizing neighbors and model parameters. For every individuals, evoPM creates a model that best predicts the outcome for this individual at the time of model creation. A comparative study is given for investigating the feasibility of the proposed system on several benchmark datasets using global, local and personalized modeling methods. The proposed evoPM system is capable of identifying a small group of the most informative features, optimizing the neighbors and model parameters relevant to the learning function (a classifier), which leads to improved classification performance. The experimental results show that evoPM not only outperforms several global and local modeling methods in terms of classification accuracy, but also finds the optimal or near-optimal solution to feature selection, and neighborhood, model parameters optimization in less number of iterations than many other evolutionary computational based optimizing algorithms.