Report about VOCs Dataset's Analysis based on randomForests Method

  • Authors:

  • Zhang Huaizhong;Fred Hamprecht;Anton Amann

  • Affiliations:

  • Nanjing Normal University Nanjing, China;IWR, Heidelberg University, Germany;Universitatsklinik Anasthesie, Leopold-Franzens-Universitat, Austria

  • Venue:
  • HPCASIA '05 Proceedings of the Eighth International Conference on High-Performance Computing in Asia-Pacific Region
  • Year:
  • 2005

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Abstract

Volatile organic compounds (VOCs) play an important role in diagnosis and therapy of various diseases. We compare several main classifiers for data classification and point out the advantages of randomForests on supervising learning. So, in this project, we take the randomForests approach to analyze and appraise the VOCs data originally coming from the medical test. According to actual situation, combining the unsupervising and supervising methods, the important components and outliers are given. The evaluation for the classifying results has been acquired due to the cross-validation sampling methods.