Feature Selection and Radial Basis Function Network for Parkinson Disease Classiﬁcation

Ibrahim, Ashraf Osman; Hussien, Walaa Akif; Yagoop, Ayat Mohammoud; Arfian, Ismail Mohd

doi:10.24017/science.2017.3.121

Cited by 1 publication

(1 citation statement)

References 19 publications

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“…Inadvisable parameter settings result in inferior classification performance. For the future work, this study can be extend into two part; firstly by improving the performance of GA such as hybrid GA with other method as works done by [22][23][24], and secondly by apply feature selection method in SVM for optimal parameter setting as proposed in [25].…”

Section: Conclusion and Recommendationmentioning

confidence: 99%

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

Hussain¹,

Ibraheem²,

Alsajri³

et al. 2020

IJECE

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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.

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