UMP Institutional Repository

A new model for iris data set classification based on linear support vector machine parameter's optimization

Faiz Hussain, Zahraa and Ibraheem, Hind Raad and Alsajri, Mohammad and Ali, Ahmed Hussein and Mohd Arfian, Ismail and Shahreen, Kasim and Sutikno, Tole (2020) A new model for iris data set classification based on linear support vector machine parameter's optimization. International Journal of Electrical and Computer Engineering (IJECE), 10 (1). pp. 1079-1084. ISSN 2088-8708

[img]
Preview
Pdf (Open access)
A new model for iris data set classification based.pdf
Available under License Creative Commons Attribution Non-commercial.

Download (504kB) | Preview

Abstract

Data mining is known as the process of detection concerning patterns from essential amounts of data. As a process of knowledge discovery. Classification is a data analysis that extracts a model which describes an important data classes. One of the outstanding classifications methods in data mining is support vector machine classification (SVM). It is capable of envisaging results and mostly effective than other classification methods. The SVM is a one technique of machine learning techniques that is well known technique, learning with supervised and have been applied perfectly to a vary problems of: regression, classification, and clustering in diverse domains such as gene expression, web text mining. In this study, we proposed a newly mode for classifying iris data set using SVM classifier and genetic algorithm to optimize c and gamma parameters of linear SVM, in addition principle components analysis (PCA) algorithm was use for features reduction.

Item Type: Article
Additional Information: Indexed by Scopus
Uncontrolled Keywords: Data mining; Classification; SVM; Genetic Algorithm; Iris Dataset; Parameter Optimization
Subjects: Q Science > QA Mathematics > QA76 Computer software
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Faculty/Division: Faculty of Computer System And Software Engineering
Depositing User: Mrs Norsaini Abdul Samat
Date Deposited: 28 Feb 2020 09:06
Last Modified: 28 Feb 2020 09:06
URI: http://umpir.ump.edu.my/id/eprint/27844
Download Statistic: View Download Statistics

Actions (login required)

View Item View Item