Machine Learning
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
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
Sum Versus Vote Fusion in Multiple Classifier Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition Letters
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Artificial Intelligence
DS'05 Proceedings of the 8th international conference on Discovery Science
Asymmetric hemisphere modeling in an offline brain-computerinterface
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Fuzzy decision trees: issues and methods
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A comparative study on heuristic algorithms for generating fuzzydecision trees
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
The random electrode selection ensemble for EEG signal classification
Pattern Recognition
The random electrode selection ensemble for EEG signal classification
Pattern Recognition
Weighting Individual Classifiers by Local Within-Class Accuracies
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II
Local within-class accuracies for weighting individual outputs in multiple classifier systems
Pattern Recognition Letters
Extreme energy difference for feature extraction of EEG signals
Expert Systems with Applications: An International Journal
Evaluation of ensemble methods for diagnosing of valvular heart disease
Expert Systems with Applications: An International Journal
Multiple-view multiple-learner active learning
Pattern Recognition
Review article: Human scalp EEG processing: Various soft computing approaches
Applied Soft Computing
Clustering technique-based least square support vector machine for EEG signal classification
Computer Methods and Programs in Biomedicine
Multiple-View Multiple-Learner Semi-Supervised Learning
Neural Processing Letters
Differential operator in seizure detection
Computers in Biology and Medicine
Computers in Biology and Medicine
InstanceRank based on borders for instance selection
Pattern Recognition
EEG signal classification using the event-related coherence and genetic algorithm
BICS'13 Proceedings of the 6th international conference on Advances in Brain Inspired Cognitive Systems
Hi-index | 0.10 |
Ensemble learning for improving weak classifiers is one important direction in the current research of machine learning, and thereinto bagging, boosting and random subspace are three powerful and popular representatives. They have so far shown efficacies in many practical classification problems. However, for electroencephalogram (EEG) signal classification with application to brain-computer interfaces (BCIs), there are almost no studies investigating their feasibilities. The present study systematically evaluates the performance of the three ensemble methods for EEG signal classification of mental imagery tasks. With the base classifiers of k-nearest-neighbor, decision tree and support vector machine, classification experiments are carried out upon real EEG recordings. Experimental results suggest the feasibilities of ensemble classification methods, and we also derive some valuable conclusions on the performance of ensemble methods for EEG signal classification.