engleski

Machine Learning Methods for EEG Signal Classification in Affective Disorders

Imbalanced datasets are often found in medical praxis. Classical machine learning algorithms suffer from overfitting on imbalanced datasets, which is why special methods are needed to improve model performance. In this thesis, EEG signal features classification by medical diagnoses was performed on several tables using the support vector machine, k-nearest neighbors, random forest and AdaBoost models. Classification results were analyzed and it was shown that the AdaBoost and linear kernel support vector machine models show the most potential in majority of the examples.

EEG ; electroencephalography ; machine learning ; imbalanced dataset ; affective disorder ; support vector machine ; k-nearest neighbors algorithm ; AdaBoost

nije evidentirano