En Yakın Komşu Algoritması Kullanılarak EEG Sinyallerine Boyut Azaltmanın Etkilerinin İncelenmesi

Authors : Duygu Kaya, Mustafa Türk, Turgay Kaya

Pages : 591-595

Doi:10.31202/ecjse.385192

View : 17 | Download : 7

Publication Date : 2018-05-31

Article Type : Research

Abstract :Machine learning which a paradigm of methods that makes inferences from existing data using mathematical and statistical methods and is inferred to be unknown. The proposed method in this paper, supervised learning algorithm is applied to EEG ( electroencephalography ) data and classification algorithm performance is analyzed and results are examined in MATLAB. K-Nearest Neighbors Algorithm (k-NN) is used in this paper as algorithm. This classification was evaluated in two stages, with and without Principal Component Analysis (PCA). Dimension reduction is the process of reducing the size of dimension of the data. By reducing the size of the data set with PCA, it is expected to protect important data features. KNN has given results that can be regarded as prudent in terms of classification accuracy. The results of the present work showed that appropriate features combined with classifier can be done significant classification for different bioelectrical signal
Keywords : Denetimli öğrenme algoritması, En yakın komşu algoritması (kNN), Temel Bileşen Analizi, Boyut Azaltma, EEG