Winsorize tree algorithm for handling outliers in classification problem

Winsorize tree algorithm for handling outliers in classification problem

Classification and Regression Tree (CART) is designed to predict or classify the objects in the predetermined classes from a set of predictors. However, having outliers could affect the structures of CART, purity and predictive accuracy in classification. Some researchers opt to perform pre-pruning...

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Main Author: Ch’ng, Chee Keong
Format: Thesis
Language:eng
eng
Published: 2016
Subjects:
QA273-280 Probabilities
Mathematical statistics
Online Access:https://etd.uum.edu.my/5780/1/depositpermission_s92068.pdf
https://etd.uum.edu.my/5780/14/s92068_01.pdf
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id my-uum-etd.5780
record_format uketd_dc
institution Universiti Utara Malaysia
collection UUM ETD
language eng
eng
advisor Ismail, Wan Rosmanira
Mahat, Nor Idayu
topic QA273-280 Probabilities
Mathematical statistics
spellingShingle QA273-280 Probabilities
Mathematical statistics
Ch’ng, Chee Keong
Winsorize tree algorithm for handling outliers in classification problem
description Classification and Regression Tree (CART) is designed to predict or classify the objects in the predetermined classes from a set of predictors. However, having outliers could affect the structures of CART, purity and predictive accuracy in classification. Some researchers opt to perform pre-pruning or post-pruning of the CART in handling the outliers. This study proposes a modified classification tree algorithm called Winsorize tree based on the distribution of classes in the training dataset. The Winsorize tree investigates all possible outliers from node to node before checking the potential splitting point to gain the node with the highest purity of the nodes. The upper fence and lower fence of a boxplot are used to detect potential outliers whose values exceeding the tail of Q ± (1.5×Interquartile range). The identified outliers are neutralized using the Winsorize method whilst the Winsorize Gini index is then used to compute the divergences among probability distributions of the target predictor’s values until stopping criteria are met. This study uses three stopping rules: node achieved the minimum 10% of total training set,
format Thesis
qualification_name Ph.D.
qualification_level Doctorate
author Ch’ng, Chee Keong
author_facet Ch’ng, Chee Keong
author_sort Ch’ng, Chee Keong
title Winsorize tree algorithm for handling outliers in classification problem
title_short Winsorize tree algorithm for handling outliers in classification problem
title_full Winsorize tree algorithm for handling outliers in classification problem
title_fullStr Winsorize tree algorithm for handling outliers in classification problem
title_full_unstemmed Winsorize tree algorithm for handling outliers in classification problem
title_sort winsorize tree algorithm for handling outliers in classification problem
granting_institution Universiti Utara Malaysia
granting_department Awang Had Salleh Graduate School of Arts & Sciences
publishDate 2016
url https://etd.uum.edu.my/5780/1/depositpermission_s92068.pdf
https://etd.uum.edu.my/5780/14/s92068_01.pdf
_version_ 1747827980338462720
spelling my-uum-etd.57802022-04-10T23:45:27Z Winsorize tree algorithm for handling outliers in classification problem 2016 Ch’ng, Chee Keong Ismail, Wan Rosmanira Mahat, Nor Idayu Awang Had Salleh Graduate School of Arts & Sciences Awang Had Salleh Graduate School of Arts and Sciences QA273-280 Probabilities. Mathematical statistics Classification and Regression Tree (CART) is designed to predict or classify the objects in the predetermined classes from a set of predictors. However, having outliers could affect the structures of CART, purity and predictive accuracy in classification. Some researchers opt to perform pre-pruning or post-pruning of the CART in handling the outliers. This study proposes a modified classification tree algorithm called Winsorize tree based on the distribution of classes in the training dataset. The Winsorize tree investigates all possible outliers from node to node before checking the potential splitting point to gain the node with the highest purity of the nodes. The upper fence and lower fence of a boxplot are used to detect potential outliers whose values exceeding the tail of Q ± (1.5×Interquartile range). The identified outliers are neutralized using the Winsorize method whilst the Winsorize Gini index is then used to compute the divergences among probability distributions of the target predictor’s values until stopping criteria are met. This study uses three stopping rules: node achieved the minimum 10% of total training set, 2016 Thesis https://etd.uum.edu.my/5780/ https://etd.uum.edu.my/5780/1/depositpermission_s92068.pdf text eng public https://etd.uum.edu.my/5780/14/s92068_01.pdf text eng public Ph.D. doctoral Universiti Utara Malaysia Abraham, B., & Ledolter, J. (2006). Introduction to regression modeling. Belmont, USA: Thomson Higher Education. 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