Ensemble learning using multi-objective optimisation for arabic handwritten words
Arabic handwriting recognition is a dynamic and stimulating field of study within pattern recognition. This system plays quite a significant part in today's global environment. It is a widespread and computationally costly function due to cursive writing, a massive number of words, and writi...
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my.uthm.eprints.84212023-02-26T07:18:59Z http://eprints.uthm.edu.my/8421/ Ensemble learning using multi-objective optimisation for arabic handwritten words Ghadhban, Haitham Qutaiba T Technology (General) Arabic handwriting recognition is a dynamic and stimulating field of study within pattern recognition. This system plays quite a significant part in today's global environment. It is a widespread and computationally costly function due to cursive writing, a massive number of words, and writing style. Based on the literature, the existing features lack data supportive techniques and building geometric features. Most ensemble learning approaches are based on the assumption of linear combination, which is not valid due to differences in data types. Also, the existing approaches of classifier generation do not support decision-making for selecting the most suitable classifier, and it requires enabling multi-objective optimisation to handle these differences in data types. In this thesis, new type of feature for handwriting using Segments Interpolation (SI) to find the best fitting line in each of the windows with a model for finding the best operating point window size for SI features. Multi-Objective Ensemble Oriented (MOEO) formulated to control the classifier topology and provide feedback support for changing the classifiers' topology and weights based on the extension of Non-dominated Sorting Genetic Algorithm (NSGA-II). It is designated as the Random Subset based Parents Selection (RSPS-NSGA-II) to handle neurons and accuracy. Evaluation metrics from two perspectives classification and Multiobjective optimization. The experimental design based on two subsets of the IFN/ENIT database. The first one consists of 10 classes (C10) and 22 classes (C22). The features were tested with Support Vector Machine (SVM) and Extreme Learning Machine (ELM). This work improved due to the SI feature. SI shows a significant result with SVM with 88.53% for C22. RSPS for C10 at k=2 achieved 91% accuracy with fewer neurons than NSGA-II, and for C22 at k=10, accuracy has been increased 81% compared to NSGA-II 78%. Future work may consider introducing more features to the system, applying them to other languages, and integrating it with sequence learning for more accuracy. 2021-12 Thesis NonPeerReviewed text en http://eprints.uthm.edu.my/8421/1/24p%20HAITHAM%20QUTAIBA%20GHADHBAN.pdf text en http://eprints.uthm.edu.my/8421/2/HAITHAM%20QUTAIBA%20GHADHBAN%20COPYRIGHT%20DECLARATION.pdf text en http://eprints.uthm.edu.my/8421/3/HAITHAM%20QUTAIBA%20GHADHBAN%20WATERMARK.pdf Ghadhban, Haitham Qutaiba (2021) Ensemble learning using multi-objective optimisation for arabic handwritten words. Doctoral thesis, Universiti Tun Hussein Onn Malaysia. |
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T Technology (General) Ghadhban, Haitham Qutaiba Ensemble learning using multi-objective optimisation for arabic handwritten words |
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Arabic handwriting recognition is a dynamic and stimulating field of study within
pattern recognition. This system plays quite a significant part in today's global
environment. It is a widespread and computationally costly function due to cursive
writing, a massive number of words, and writing style. Based on the literature, the
existing features lack data supportive techniques and building geometric features.
Most ensemble learning approaches are based on the assumption of linear
combination, which is not valid due to differences in data types. Also, the existing
approaches of classifier generation do not support decision-making for selecting the
most suitable classifier, and it requires enabling multi-objective optimisation to handle
these differences in data types. In this thesis, new type of feature for handwriting using
Segments Interpolation (SI) to find the best fitting line in each of the windows with a
model for finding the best operating point window size for SI features. Multi-Objective
Ensemble Oriented (MOEO) formulated to control the classifier topology and provide
feedback support for changing the classifiers' topology and weights based on the
extension of Non-dominated Sorting Genetic Algorithm (NSGA-II). It is designated
as the Random Subset based Parents Selection (RSPS-NSGA-II) to handle neurons
and accuracy. Evaluation metrics from two perspectives classification and Multiobjective
optimization. The experimental design based on two subsets of the
IFN/ENIT database. The first one consists of 10 classes (C10) and 22 classes (C22).
The features were tested with Support Vector Machine (SVM) and Extreme Learning
Machine (ELM). This work improved due to the SI feature. SI shows a significant
result with SVM with 88.53% for C22. RSPS for C10 at k=2 achieved 91% accuracy
with fewer neurons than NSGA-II, and for C22 at k=10, accuracy has been increased
81% compared to NSGA-II 78%. Future work may consider introducing more features
to the system, applying them to other languages, and integrating it with sequence
learning for more accuracy. |
format |
Thesis |
author |
Ghadhban, Haitham Qutaiba |
author_facet |
Ghadhban, Haitham Qutaiba |
author_sort |
Ghadhban, Haitham Qutaiba |
title |
Ensemble learning using multi-objective optimisation for arabic handwritten words |
title_short |
Ensemble learning using multi-objective optimisation for arabic handwritten words |
title_full |
Ensemble learning using multi-objective optimisation for arabic handwritten words |
title_fullStr |
Ensemble learning using multi-objective optimisation for arabic handwritten words |
title_full_unstemmed |
Ensemble learning using multi-objective optimisation for arabic handwritten words |
title_sort |
ensemble learning using multi-objective optimisation for arabic handwritten words |
publishDate |
2021 |
url |
http://eprints.uthm.edu.my/8421/1/24p%20HAITHAM%20QUTAIBA%20GHADHBAN.pdf http://eprints.uthm.edu.my/8421/2/HAITHAM%20QUTAIBA%20GHADHBAN%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/8421/3/HAITHAM%20QUTAIBA%20GHADHBAN%20WATERMARK.pdf http://eprints.uthm.edu.my/8421/ |
_version_ |
1758952405639102464 |
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13.18916 |