Abstract: The paper presents different methods of combining many neural classifiers into one ense... more Abstract: The paper presents different methods of combining many neural classifiers into one ensemble system for recognition and classification of arrhythmia. Majority and weighted voting, Kullback-Leibler divergence and modified Bayes methods will be presented and ...
The present paper focuses on developing and testing various reliable and robust tools with the ai... more The present paper focuses on developing and testing various reliable and robust tools with the aim of managing energy sources and promoting renewable energy for the city of Perpignan (south of France). In this sense, forecasting average wind speeds was the main objective ...
International Journal of Applied Mathematics and Computer Science, 2014
The paper presents a new system for ECG (ElectroCardioGraphy) signal recognition using different ... more The paper presents a new system for ECG (ElectroCardioGraphy) signal recognition using different neural classifiers and a binary decision tree to provide one more processing stage to give the final recognition result. As the base classifiers, the three classical neural models, i.e., the MLP (Multi Layer Perceptron), modified TSK (Takagi-Sugeno-Kang) and the SVM (Support Vector Machine), will be applied. The coefficients in ECG signal decomposition using Hermite basis functions and the peak-to-peak periods of the ECG signals will be used as features for the classifiers. Numerical experiments will be performed for the recognition of different types of arrhythmia in the ECG signals taken from the MIT-BIH (Massachusetts Institute of Technology and Boston’s Beth Israel Hospital) Arrhythmia Database. The results will be compared with individual base classifiers’ performances and with other integration methods to show the high quality of the proposed solution
The paper presents different methods of combining many neural classifiers into one ensemble syste... more The paper presents different methods of combining many neural classifiers into one ensemble system for recognition and classification of arrhythmia. Majority and weighted voting, Kullback–Leibler divergence and modified Bayes methods will be presented and compared. The numerical experiments will be performed for the problems concerning the recognition of different types of arrhythmia on the basis of ECG waveforms of MIT BIH AD. The results have shown that combining many classifiers into one classification system brings important benefits and result in significant improvement of accuracy.
Abstract: The paper presents different methods of combining many neural classifiers into one ense... more Abstract: The paper presents different methods of combining many neural classifiers into one ensemble system for recognition and classification of arrhythmia. Majority and weighted voting, Kullback-Leibler divergence and modified Bayes methods will be presented and ...
The present paper focuses on developing and testing various reliable and robust tools with the ai... more The present paper focuses on developing and testing various reliable and robust tools with the aim of managing energy sources and promoting renewable energy for the city of Perpignan (south of France). In this sense, forecasting average wind speeds was the main objective ...
International Journal of Applied Mathematics and Computer Science, 2014
The paper presents a new system for ECG (ElectroCardioGraphy) signal recognition using different ... more The paper presents a new system for ECG (ElectroCardioGraphy) signal recognition using different neural classifiers and a binary decision tree to provide one more processing stage to give the final recognition result. As the base classifiers, the three classical neural models, i.e., the MLP (Multi Layer Perceptron), modified TSK (Takagi-Sugeno-Kang) and the SVM (Support Vector Machine), will be applied. The coefficients in ECG signal decomposition using Hermite basis functions and the peak-to-peak periods of the ECG signals will be used as features for the classifiers. Numerical experiments will be performed for the recognition of different types of arrhythmia in the ECG signals taken from the MIT-BIH (Massachusetts Institute of Technology and Boston’s Beth Israel Hospital) Arrhythmia Database. The results will be compared with individual base classifiers’ performances and with other integration methods to show the high quality of the proposed solution
The paper presents different methods of combining many neural classifiers into one ensemble syste... more The paper presents different methods of combining many neural classifiers into one ensemble system for recognition and classification of arrhythmia. Majority and weighted voting, Kullback–Leibler divergence and modified Bayes methods will be presented and compared. The numerical experiments will be performed for the problems concerning the recognition of different types of arrhythmia on the basis of ECG waveforms of MIT BIH AD. The results have shown that combining many classifiers into one classification system brings important benefits and result in significant improvement of accuracy.
Uploads