ArticlePsychiatry investigation2014
Ant Colony Optimization Based Feature Selection Method for QEEG Data Classification.
Article in Psychiatry investigation, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Advanced EEG signal classification for neural prosthetic devices using metaheuristic and deep learning techniques.Frontiers in digital health · 2025Article
- An Enhanced Ant Colony Optimization Mechanism for the Classification of Depressive Disorders.Computational intelligence and neuroscience · 2022Article
- Article
- Ant colony optimization-based firewall anomaly mitigation engine.SpringerPlus · 2016Article
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4 authors.
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Abstract
objectiveMany 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.
methodsFeature 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.
resultsBPNN 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.
conclusionACO 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.
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