Name
Title | ||
|---|---|---|
Multi-class f-score feature selection approach to classification of obstructive sleep apnea syndrome |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1016/j.eswa.2009.05.075 | Expert Syst. Appl. |
Keywords | Field | DocType |
serious osas,multi-layer perceptron artificial neural network,classification accuracy,f-score feature selection method,apnea syndrome,different disorder degree,polysomnography,f-score feature selection,feature selection process,osas dataset,multi-class f-score feature selection,obstructive sleep apnea syndrome (osas),mild osas,moderate osas,artificial neural network,feature selection,pattern recognition,multi layer perceptron | Obstructive sleep apnea,Sleep apnea,Feature selection,Computer science,Artificial intelligence,Artificial neural network,Classifier (linguistics),Polysomnography,F1 score,Pattern recognition,Speech recognition,Perceptron,Machine learning | Journal |
Volume | Issue | ISSN |
37 | 2 | Expert Systems With Applications |
Citations | PageRank | References |
11 | 1.04 | 7 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Salih Güneş | 1 | 1267 | 78.53 |
| Kemal Polat | 2 | 1348 | 97.38 |
| Sebnem Yosunkaya | 3 | 109 | 7.90 |