학술논문
Ant Colony Optimization Based Feature Selection Method for QEEG Data Classification
이용수 2
- 영문명
- 발행기관
- 대한신경정신의학회
- 저자명
- Turker Tekin Erguzel Serhat Ozekes Selahattin Gultekin Nevzat Tarhan
- 간행물 정보
- 『Psychiatry Investigation』제11권 제3호, 243~250쪽, 전체 8쪽
- 주제분류
- 의약학 > 정신과학
- 파일형태
- 발행일자
- 2014.07.31
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국문 초록
영문 초록
Objective-Many applications such as biomedical signals require selecting a subset of the input features in order to represent the whole set of features. A feature selection algorithm has recently been proposed as a new approach for feature subset selection.
Methods-Feature selection process using ant colony optimization (ACO) for 6 channel pre-treatment electroencephalogram (EEG) data from theta and delta frequency bands is combined with back propagation neural network (BPNN) classification method for 147 major depressive disorder (MDD) subjects.
Results-BPNN classified R subjects with 91.83% overall accuracy and 95.55% subjects detection sensitivity. Area under ROC curve (AUC) value after feature selection increased from 0.8531 to 0.911. The features selected by the optimization algorithm were Fp1, Fp2, F7, F8, F3 for theta frequency band and eliminated 7 features from 12 to 5 feature subset.
Conclusion-ACO feature selection algorithm improves the classification accuracy of BPNN. Using other feature selection algorithms or classifiers to compare the performance for each approach is important to underline the validity and versatility of the designed combination.
목차
INTRODUCTION
METHODS
RESULTS
DISCUSSION
ACKNOWLEDGEMENTS
REFERENCES
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