Modified Bagging in Linear Discriminant Analysis: Machine Learning
The main idea of this work is the use of machine learning of BAGGING or Bootstrap AGGregatiING, which is extended to average the classifiers based on a distance function. The idea of this function is to find the shortest distance from each data point to the classification boundary by using ‘Manhatta...
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| Formato: | Capítulo de libro |
| Lenguaje: | inglés |
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IntechOpen
2024
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| Acceso en línea: | https://www.intechopen.com/chapters/88694 |
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| _version_ | 1879164815484649472 |
|---|---|
| author | Gimati, Yousef El |
| author_browse | Gimati, Yousef El |
| author_facet | Gimati, Yousef El |
| author_sort | Gimati, Yousef El |
| building | IntechOpen |
| collection | Acceso abierto |
| description | The main idea of this work is the use of machine learning of BAGGING or Bootstrap AGGregatiING, which is extended to average the classifiers based on a distance function. The idea of this function is to find the shortest distance from each data point to the classification boundary by using ‘Manhattan’ distance in decision trees and alternative distance measure is the ‘Mahalanobis’ distance used for Linear Discriminant Analysis or LDA, called modified bagging in this work. Thus providing a weighted voting system instead of equal weight voting, the classification error is reduced. Modified bagging is a viable option to reduce the variance which is a component of the classification error. Referring to the analysis, we conclude that modified bagging gives statistically significant improvement in Ripley’s data set with different bootstrap sample sizes. |
| doi_str_mv | 10.5772/intechopen.113260 |
| first_indexed | 2026-10-15T21:40:20Z |
| format | Book Chapter |
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<dc:title>Modified Bagging in Linear Discriminant Analysis: Machine Learning</dc:title>
<dc:creator>Yousef El Gimati</dc:creator>
<dc:subject>Physical Sciences, Engineering and Technology</dc:subject>
<dc:description>The main idea of this work is the use of machine learning of BAGGING or Bootstrap AGGregatiING, which is extended to average the classifiers based on a distance function. The idea of this function is to find the shortest distance from each data point to the classification boundary by using ‘Manhattan’ distance in decision trees and alternative distance measure is the ‘Mahalanobis’ distance used for Linear Discriminant Analysis or LDA, called modified bagging in this work. Thus providing a weighted voting system instead of equal weight voting, the classification error is reduced. Modified bagging is a viable option to reduce the variance which is a component of the classification error. Referring to the analysis, we conclude that modified bagging gives statistically significant improvement in Ripley’s data set with different bootstrap sample sizes.</dc:description>
<dc:publisher>IntechOpen</dc:publisher>
<dc:date>2024-05-02</dc:date>
<dc:type>Chapter, Part Of Book</dc:type>
<dc:identifier>https://www.intechopen.com/chapters/88694</dc:identifier>
<dc:identifier>doi:10.5772/intechopen.113260</dc:identifier>
<dc:language>en</dc:language>
<dc:relation>ISBN:978-1-83769-078-7</dc:relation>
<dc:rights>https://creativecommons.org/licenses/by/3.0/</dc:rights>
<dc:source>https://www.intechopen.com/books/12615 ; Research Advances in Data Mining Techniques and Applications</dc:source>
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| id | intech-88694 |
| institution | IntechOpen |
| isbn | 978-1-83769-078-7 |
| language | English |
| last_indexed | 2026-10-15T21:40:20Z |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | IntechOpen |
| publisherStr | IntechOpen |
| record_format | oai_dc |
| spelling | intech-886942024-05-02 Modified Bagging in Linear Discriminant Analysis: Machine Learning Yousef El Gimati Physical Sciences, Engineering and Technology The main idea of this work is the use of machine learning of BAGGING or Bootstrap AGGregatiING, which is extended to average the classifiers based on a distance function. The idea of this function is to find the shortest distance from each data point to the classification boundary by using ‘Manhattan’ distance in decision trees and alternative distance measure is the ‘Mahalanobis’ distance used for Linear Discriminant Analysis or LDA, called modified bagging in this work. Thus providing a weighted voting system instead of equal weight voting, the classification error is reduced. Modified bagging is a viable option to reduce the variance which is a component of the classification error. Referring to the analysis, we conclude that modified bagging gives statistically significant improvement in Ripley’s data set with different bootstrap sample sizes. IntechOpen 2024-05-02 Chapter, Part Of Book https://www.intechopen.com/chapters/88694 doi:10.5772/intechopen.113260 en ISBN:978-1-83769-078-7 https://creativecommons.org/licenses/by/3.0/ https://www.intechopen.com/books/12615 ; Research Advances in Data Mining Techniques and Applications |
| spellingShingle | Physical Sciences, Engineering and Technology Gimati, Yousef El Modified Bagging in Linear Discriminant Analysis: Machine Learning |
| title | Modified Bagging in Linear Discriminant Analysis: Machine Learning |
| title_full | Modified Bagging in Linear Discriminant Analysis: Machine Learning |
| title_fullStr | Modified Bagging in Linear Discriminant Analysis: Machine Learning |
| title_full_unstemmed | Modified Bagging in Linear Discriminant Analysis: Machine Learning |
| title_short | Modified Bagging in Linear Discriminant Analysis: Machine Learning |
| title_sort | modified bagging in linear discriminant analysis machine learning |
| topic | Physical Sciences, Engineering and Technology |
| url | https://www.intechopen.com/chapters/88694 |
| work_keys_str_mv | AT gimatiyousefel modifiedbagginginlineardiscriminantanalysismachinelearning |