An improved algorithm for iris classification by using support vector machine and binary random machine learning

In machine learning, there are three type of learning branch that can used in classification procedures for data mining. Those branch are consist of supervised learning, unsupervised learning and reinforcement learning. This study focuses on supervised learning that seek to classify all the Iris dat...

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Main Author:	Kamarulzalis, Ahmad Haadzal
Format:	Thesis
Language:	English English English
Published:	2018
Subjects:	Q300-390 Cybernetics
Online Access:	http://eprints.uthm.edu.my/295/1/24p%20ahmad%20haadzal%20kamarulzalis.pdf http://eprints.uthm.edu.my/295/2/AHMAD%20HAADZAL%20KAMARULZALIS%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/295/3/AHMAD%20HAADZAL%20KAMARULZALIS%20WATERMARK.pdf
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spelling	my-uthm-ep.2952021-07-21T03:09:02Z An improved algorithm for iris classification by using support vector machine and binary random machine learning 2018-07 Kamarulzalis, Ahmad Haadzal Q300-390 Cybernetics In machine learning, there are three type of learning branch that can used in classification procedures for data mining. Those branch are consist of supervised learning, unsupervised learning and reinforcement learning. This study focuses on supervised learning that seek to classify all the Iris dataset respect to three species (setosa, versicolor and virginica) in order them to mimic the actual dataset by using Support Vector Machine with four different kernel function (Linear, Radial Basis, Sigmoid and Polynomial), Random Forest (RF), k-Nearest Neighbors(k-NN) and Random Nearest Neighbors (RNN) as a method. The first objective of this study is to improve a new algorithm technique for classification. The new algorithm come from a combination of an ideas of k-NN algorithm and ensemble concept. The second objective is to conduct a supervised and binary ensemble machine learning technique for classification. This is done by using method of RF and RNN that share the same ensemble concept. The last objective is to identify the best model for classification procedures. Performance Measurement Tools such as overall accuracy, kappa, average sensitivity, average specificity, average precious, average detection rate, average prevalence and misclassification error rate (MER) were used by refers confusion matrix values output during data analysis for average and individual performance of each classifier. Besides that, Performance Visualization such as Stacked Bar Plot, Fourfold Plot, Receiver Operating Characteristic (ROC) Curve and Lollipop Chart are used to simplify each output for more clear understanding. Random Nearest Neighbors (RNN) has highest accuracy value that is 98.67% and just 1.33% misclassification error rate (MER) compare to other classifier. Therefore, Random Nearest Neighbors (RNN) is preferable for supervised learning classification procedures. 2018-07 Thesis http://eprints.uthm.edu.my/295/ http://eprints.uthm.edu.my/295/1/24p%20ahmad%20haadzal%20kamarulzalis.pdf text en public http://eprints.uthm.edu.my/295/2/AHMAD%20HAADZAL%20KAMARULZALIS%20COPYRIGHT%20DECLARATION.pdf text en staffonly http://eprints.uthm.edu.my/295/3/AHMAD%20HAADZAL%20KAMARULZALIS%20WATERMARK.pdf text en validuser mphil masters Universiti Tun Hussein Onn Malaysia Faculty of Applied Sciences and Technology
institution	Universiti Tun Hussein Onn Malaysia
collection	UTHM Institutional Repository
language	English English English
topic	Q300-390 Cybernetics
spellingShingle	Q300-390 Cybernetics Kamarulzalis, Ahmad Haadzal An improved algorithm for iris classification by using support vector machine and binary random machine learning
description	In machine learning, there are three type of learning branch that can used in classification procedures for data mining. Those branch are consist of supervised learning, unsupervised learning and reinforcement learning. This study focuses on supervised learning that seek to classify all the Iris dataset respect to three species (setosa, versicolor and virginica) in order them to mimic the actual dataset by using Support Vector Machine with four different kernel function (Linear, Radial Basis, Sigmoid and Polynomial), Random Forest (RF), k-Nearest Neighbors(k-NN) and Random Nearest Neighbors (RNN) as a method. The first objective of this study is to improve a new algorithm technique for classification. The new algorithm come from a combination of an ideas of k-NN algorithm and ensemble concept. The second objective is to conduct a supervised and binary ensemble machine learning technique for classification. This is done by using method of RF and RNN that share the same ensemble concept. The last objective is to identify the best model for classification procedures. Performance Measurement Tools such as overall accuracy, kappa, average sensitivity, average specificity, average precious, average detection rate, average prevalence and misclassification error rate (MER) were used by refers confusion matrix values output during data analysis for average and individual performance of each classifier. Besides that, Performance Visualization such as Stacked Bar Plot, Fourfold Plot, Receiver Operating Characteristic (ROC) Curve and Lollipop Chart are used to simplify each output for more clear understanding. Random Nearest Neighbors (RNN) has highest accuracy value that is 98.67% and just 1.33% misclassification error rate (MER) compare to other classifier. Therefore, Random Nearest Neighbors (RNN) is preferable for supervised learning classification procedures.
format	Thesis
qualification_name	Master of Philosophy (M.Phil.)
qualification_level	Master's degree
author	Kamarulzalis, Ahmad Haadzal
author_facet	Kamarulzalis, Ahmad Haadzal
author_sort	Kamarulzalis, Ahmad Haadzal
title	An improved algorithm for iris classification by using support vector machine and binary random machine learning
title_short	An improved algorithm for iris classification by using support vector machine and binary random machine learning
title_full	An improved algorithm for iris classification by using support vector machine and binary random machine learning
title_fullStr	An improved algorithm for iris classification by using support vector machine and binary random machine learning
title_full_unstemmed	An improved algorithm for iris classification by using support vector machine and binary random machine learning
title_sort	improved algorithm for iris classification by using support vector machine and binary random machine learning
granting_institution	Universiti Tun Hussein Onn Malaysia
granting_department	Faculty of Applied Sciences and Technology
publishDate	2018
url	http://eprints.uthm.edu.my/295/1/24p%20ahmad%20haadzal%20kamarulzalis.pdf http://eprints.uthm.edu.my/295/2/AHMAD%20HAADZAL%20KAMARULZALIS%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/295/3/AHMAD%20HAADZAL%20KAMARULZALIS%20WATERMARK.pdf
_version_	1747830575689891840

An improved algorithm for iris classification by using support vector machine and binary random machine learning

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