Multi-Class F-Score Feature Selection Approach to Classification of Obstructive Sleep Apnea Syndrome
Yükleniyor...
Dosyalar
Tarih
2010
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
PERGAMON-ELSEVIER SCIENCE LTD
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
In this paper, a new feature selection named as multi-class f-score feature selection is proposed for sleep apnea classification having different disorder degrees (mild OSAS, moderate OSAS, serious OSAS, and non-OSAS). f-Score is used to measure the discriminating power of features in the classification of two-class pattern recognition problems. In order to apply the f-score feature selection to multi-class datasets, we have used the f-score feature selection as pairwise (in the form of two classes) in the diagnosis of obstructive sleep apnea syndrome (OSAS) with four classes. After feature selection process, MLPANN (Multi-layer perceptron artificial neural network) classifier is used to diagnose the OSAS having different disorder degrees. While MLPANN obtained 63.41% classification accuracy on the diagnosis of OSAS, the combination of MLPANN and multi-class f-score feature selection achieved 84.14% classification accuracy using 50-50% training-testing split of OSAS dataset with four classes. These results demonstrate that the proposed multi-class f-score feature selection method is effective and robust in determining the disorder degrees of OSAS.
Açıklama
Anahtar Kelimeler
Obstructive Sleep Apnea Syndrome (OSAS), Multi-Class F-Score Feature Selection, Multi-Layer Perceptron Artificial Neural Network, Polysomnography
Kaynak
Expert Systems with Applications
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
37
Sayı
2
Künye
Güneş, S., Polat, K., Yosunkaya, Ş., (2010). Multi-Class F-Score Feature Selection Approach to Classification of Obstructive Sleep Apnea Syndrome. Expert Systems with Applications, 37(2), 998-1004.
DOI: 10.1016/j.eswa.2009.05.075
