Class Specific Feature Selection Using Simulated Annealing

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• 刊名：Lecture Notes in Computer Science
• 出版年：2015
• 出版时间：2015
• 年：2015
• 卷：9468
• 期：1
• 页码：12-21
• 全文大小：293 KB
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• 作者单位：V. Susheela Devi (16)
  
  16. Department of Computer Science and Automation, Indian Institute of Science, Bangalore, 560 012, India
  
• 丛书名：Mining Intelligence and Knowledge Exploration
• ISBN：978-3-319-26832-3
• 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

This paper proposes a method of identifying features which are important for each class. This entails selecting the features specifically for each class. This is carried out by using the simulated annealing technique. The algorithm is run separately for each class resulting in the feature subset for that class. A test pattern is classified by running a classifier for each class and combining the result. The 1NN classifier is the classification algorithm used. Results have been reported on eight benchmark datasets from the UCI repository. The selected features, besides giving good classification accuracy, gives an idea of the important features for each class.