Report about VOCs dataset´s analysis based on randomForests method

Author

Huaizhong, Zhang ; Hamprecht, Fred ; Amann, Anton

Author_Institution

Sch. of Math. & Comput. Sci., Nanjing Normal Univ.

fYear

2005

fDate

1-1 July 2005

Lastpage

607

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

Keywords

classification; data analysis; diseases; learning (artificial intelligence); medical computing; patient diagnosis; sampling methods; VOC datasets analysis; cross-validation sampling methods; data classification; disease diagnosis; disease therapy; randomForests method; supervising learning; unsupervising learning; volatile organic compounds; Data analysis; Decision theory; Diseases; Gas detectors; Humans; Mathematics; Medical diagnostic imaging; Medical treatment; Pattern analysis; Volatile organic compounds;

fLanguage

English

Publisher

ieee

Conference_Titel

High-Performance Computing in Asia-Pacific Region, 2005. Proceedings. Eighth International Conference on

Conference_Location

Beijing

Print_ISBN

0-7695-2486-9

Type

conf

DOI

10.1109/HPCASIA.2005.85

Filename

1592328