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


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.


Data mining; Classification; SVM; Genetic Algorithm; Iris Dataset; Parameter Optimization

Full Text:


DOI: http://doi.org/10.11591/ijece.v10i1.pp1079-1084

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

International Journal of Electrical and Computer Engineering (IJECE)
p-ISSN 2088-8708, e-ISSN 2722-2578

This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).