Classification in the missing data

• Author / Creator

  Zhang, Xin

• Missing data is always a problem when it comes to data analysis. This is especially the case in anthropology when sex determination is one of the primary goals for fossil skull data since many measurements were not available. We expect to find a classifier that can handle the large amount of missingness and improve the ability of prediction/classification as well. These are the objectives of this thesis.

  Besides of the crude methods (ignore cases with missingness), three possible techniques in handling of missing values are discussed: bootstrap imputation, weighted-averaging classifier and classification trees. All these methods do make use of all the cases in data and can handle any cases with missingness.

  The diabetes data and fossil skull data are used to compare the performance of different methods regarding to misclassification error rate. Each method has its own advantages and certain situations under which better performance will be achieved.

• Subjects / Keywords

  • Research--Methodology
  • Missing observations (Statistics)

• Graduation date

  Fall 2010

• Type of Item

  Thesis

• Degree

  Master of Science

• DOI

  https://doi.org/10.7939/R34H89

• License

  This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.