Sensitivity of missing values in classification tree for large sample

Citation

Hasan, Norsida and Adam, Mohd Bakri and Mustapha, Norwati and Abu Bakar, Mohd Rizam (2011) Sensitivity of missing values in classification tree for large sample. In: 5th International Conference on Research and Education in Mathematics (ICREM5), 22-24 Oct. 2011, Bandung, Indonesia. (pp. 374-379).

Abstract

Missing values either in predictor or in response variables are a very common problem in statistics and data mining. Cases with missing values are often ignored which results in loss of information and possible bias. The objectives of our research were to investigate the sensitivity of missing data in classification tree model for large sample. Data were obtained from one of the high level educational institutions in Malaysia. Students' background data were randomly eliminated and classification tree was used to predict students degree classification. The results showed that for large sample, the structure of the classification tree was sensitive to missing values especially for sample contains more than ten percent missing values.

Download File

Preview

PDF (Abstract)
Sensitivity of missing values in classification tree for large sample.pdf
Download (34kB) | Preview

Additional Metadata

Item Type:	Conference or Workshop Item (Paper)
Divisions:	Institute for Mathematical Research
DOI Number:	https://doi.org/10.1063/1.4724171
Publisher:	American Institute of Physics
Keywords:	Classification tree; Missing data
Depositing User:	Nabilah Mustapa
Date Deposited:	26 Sep 2017 04:07
Last Modified:	26 Sep 2017 04:07
Altmetrics:	http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.1063/1.4724171
URI:	http://psasir.upm.edu.my/id/eprint/57334
Statistic Details:	View Download Statistic

Actions (login required)

View Item