Open AccessA new model for iris data set classification based on linear support vector machine parameter's optimization
Author(s) -
Zahraa Faiz Hussain,
Hind Raad Ibraheem,
Mohammad Alsajri,
Ahmed Hussein Ali,
Mohd Arfian Ismail,
Shahreen Kasim,
Tole Sutikno
Publication year - 2020
Publication title -
international journal of electrical and computer engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.277
H-Index - 22
ISSN - 2088-8708
DOI - 10.11591/ijece.v10i1.pp1079-1084
Subject(s) - support vector machine , computer science , cluster analysis , artificial intelligence , data mining , linear classifier , machine learning , classifier (uml) , data set , pattern recognition (psychology) , structured support vector machine , relevance vector machine , data classification
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.