A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Fractional-Step Dimensionality Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Upper Bounds for Error Rates of Linear Combinations of Classifiers
IEEE Transactions on Pattern Analysis and Machine Intelligence
Kolmogorov Complexity of Finite Sequences and Recognition of Different Preictal EEG Patterns
CBMS '95 Proceedings of the Eighth Annual IEEE Symposium on Computer-Based Medical Systems
Linear correlation between fractal dimension of EEG signal and handgrip force
Biological Cybernetics
Journal of Network and Computer Applications - Special issue: Network and information security: A computational intelligence approach
A proposed method for learning rule weights in fuzzy rule-based classification systems
Fuzzy Sets and Systems
Fuzzy multi-class classifier based on support vector data description and improved PCM
Expert Systems with Applications: An International Journal
Ensemble-based discriminant learning with boosting for face recognition
IEEE Transactions on Neural Networks
Comparative clustering analysis of bispectral index series of brain activity
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Fuzzy Hopfield neural network clustering for single-trial motor imagery EEG classification
Expert Systems with Applications: An International Journal
Review article: Human scalp EEG processing: Various soft computing approaches
Applied Soft Computing
Engineering Applications of Artificial Intelligence
International Journal of Mobile Learning and Organisation
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In this paper, electroencephalogram (EEG) signals of 13 schizophrenic patients and 18 age-matched control participants are analyzed with the objective of classifying the two groups. For each case, multi-channels (22 electrodes) scalp EEG is recorded. Several features including autoregressive (AR) model parameters, band power and fractal dimension are extracted from the recorded signals. Leave-one (participant)-out cross validation is used to have an accurate estimation for the separability of the two groups. Boosted version of Direct Linear Discriminant Analysis (BDLDA) is selected as an efficient classifier which applied on the extracted features. To have comparison, classifiers such as standard LDA, Adaboost, support vector machine (SVM), and fuzzy SVM (FSVM) are applied on the features. Results show that the BDLDA is more discriminative than others such that their classification rates are reported 87.51%, 85.36% and 85.41% for the BDLDA, LDA, Adaboost, respectively. Results of SVM and FSVM classifiers were lower than 50% accuracy because they are more sensitive to outlier instances. In order to determine robustness of the suggested classifier, noises with different amplitudes are added to the test feature vectors and robustness of the BDLDA was higher than the other compared classifiers.