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All Journal International Journal of Electrical and Computer Engineering
Ahmed Hussein Ali
AL Salam University College and Al-Iraqia University
Computer Science & IT Electrical & Electronics Engineering

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A new model for iris data set classification based on linear support vector machine parameter's optimization Zahraa Faiz Hussain; Hind Raad Ibraheem; Mohammad Alsajri; Ahmed Hussein Ali; Mohd Arfian Ismail; Shahreen Kasim; Tole Sutikno
International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 1: February 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (504.778 KB) | DOI: 10.11591/ijece.v10i1.pp1079-1084

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.
Co-Authors Hind Raad Ibraheem Mohammad Alsajri Mohd Arfian Ismail Shahreen Kasim Tole Sutikno Zahraa Faiz Hussain