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

Zahraa Faiz Hussain (AL Salam University College)
Hind Raad Ibraheem (AL Salam University College)
Mohammad Alsajri (AL Salam University College and Al-Iraqia University)
Ahmed Hussein Ali (AL Salam University College and Al-Iraqia University)
Mohd Arfian Ismail (Universiti Malaysia Pahang)
Shahreen Kasim (Universiti Tun Hussein Onn Malaysia)
Tole Sutikno (Universitas Ahmad Dahlan)

International Journal of Electrical and Computer Engineering
Vol 10, No 1: February 2020

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.