relief algorithm
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Author(s):  
YASMINE BENCHAIB

Electroencephalogram (EEG) is a fundamental and unique tool for exploring human brain activity in general and epileptic mechanism in particular. It offers significant information about epileptic seizures source known as epileptogenic area. However, it is often complicated to detect critical changes in EEG signals by visual examination, since this signal aspect of epileptic persons seems to be normal out of the seizure. Thus, the challenge is to design such a robust and automatic system to detect these unseen changes and use them for diagnosis. In this research, we apply the Artificial Metaplasticity Multi-Layer Perceptron (AMMLP) together with discrete wavelet transform (DWT) to Bonn EEG signals for seizure detection goal. Significant features were then extracted from the well-known EEG brainwaves. Aiming to decrease the computational time and improve classification accuracy, we performed a features ranking and selection employing the Relief algorithm. The obtained AMMLP classification accuracy of 98.97% proved the effctiveness of the applied approach. Our results were compared to recent researches results on the same database, proving to be superior or at least an interesting alternative for seizures detection within EEG signals.


Information ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 228
Author(s):  
Hongbin Wang ◽  
Pengming Wang ◽  
Shengchun Deng ◽  
Haoran Li

As the classic feature selection algorithm, the Relief algorithm has the advantages of simple computation and high efficiency, but the algorithm itself is limited to only dealing with binary classification problems, and the comprehensive distinguishing ability of the feature subsets composed of the former K features selected by the Relief algorithm is often redundant, as the algorithm cannot select the ideal feature subset. When calculating the correlation and redundancy between characteristics by mutual information, the computation speed is slow because of the high computational complexity and the method’s need to calculate the probability density function of the corresponding features. Aiming to solve the above problems, we first improve the weight of the Relief algorithm, so that it can be used to evaluate a set of candidate feature sets. Then we use the improved joint mutual information evaluation function to replace the basic mutual information computation and solve the problem of computation speed and correlation, and redundancy between features. Finally, a compound correlation feature selection algorithm based on Relief and joint mutual information is proposed using the evaluation function and the heuristic sequential forward search strategy. This algorithm can effectively select feature subsets with small redundancy and strong classification characteristics, and has the excellent characteristics of faster calculation speed.


2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Qi Zhenya ◽  
Zuoru Zhang

Abstract Background Heart disease is the primary cause of morbidity and mortality in the world. It includes numerous problems and symptoms. The diagnosis of heart disease is difficult because there are too many factors to analyze. What’s more, the misclassification cost could be very high. Methods A cost-sensitive ensemble method was proposed to improve the efficiency of diagnosis and reduce the misclassification cost. The proposed method contains five heterogeneous classifiers: random forest, logistic regression, support vector machine, extreme learning machine and k-nearest neighbor. T-test was used to investigate if the performance of the ensemble was better than individual classifiers and the contribution of Relief algorithm. Results The best performance was achieved by the proposed method according to ten-fold cross validation. The statistical tests demonstrated that the performance of the proposed ensemble was significantly superior to individual classifiers, and the efficiency of classification was distinctively improved by Relief algorithm. Conclusions The proposed ensemble gained significantly better results compared with individual classifiers and previous studies, which implies that it can be used as a promising alternative tool in medical decision making for heart disease diagnosis.


2021 ◽  
Author(s):  
Zhenya Qi ◽  
Zuoru Zhang

Abstract Background: Heart disease is the primary cause of morbidity and mortality in the world. It includes numerous problems and symptoms. The diagnosis of heart disease is difficult because there are too many factors to analyze. What's more, the misclassification cost could be very high. Methods: A cost-sensitive ensemble method was proposed to improve the efficiency of diagnosis and reduce the misclassification cost. The proposed method contains five heterogeneous classifiers: random forest, logistic regression, support vector machine, extreme learning machine and k-nearest neighbor. T-test was used to investigate if the performance of the ensemble was better than individual classifiers and the contribution of Relief algorithm. Results: The best performance was achieved by the proposed method according to ten-fold cross validation. The statistical tests demonstrated that the performance of the proposed ensemble was significantly superior to individual classifiers, and the efficiency of classification was distinctively improved by Relief algorithm. Conclusions: The proposed ensemble gained significantly better results compared with individual classifiers and previous studies, which implies that it can be used as a promising alternative tool in medical decision making for heart disease diagnosis.


2020 ◽  
Author(s):  
Zhenya Qi ◽  
Zuoru Zhang

Abstract Background: Heart disease is the primary cause of morbidity and mortality in the world. It includes numerous problems and symptoms. The diagnosis of heart disease is difficult because there are too many factors to analyze. What's more, the misclassification cost could be very high. Methods: A cost-sensitive ensemble method was proposed to improve the efficiency of diagnosis and reduce the misclassification cost. The proposed method contains five heterogeneous classifiers: random forest, logistic regression, support vector machine, extreme learning machine and k-nearest neighbor. T-test was used to investigate if the performance of the ensemble was better than individual classifiers and the contribution of Relief algorithm. Results: The best performance was achieved by the proposed method according to ten-fold cross validation. The statistical tests demonstrated that the performance of the proposed ensemble was significantly superior to individual classifiers, and the efficiency of classification was distinctively improved by Relief algorithm. Conclusions: The proposed ensemble gained significantly better results compared with individual classifiers and previous studies, which implies that it can be used as a promising alternative tool in medical decision making for heart disease diagnosis.


2020 ◽  
Author(s):  
Zhenya Qi ◽  
Zuoru Zhang

Abstract Background: Heart disease is the primary cause of morbidity and mortality in the world. It includes numerous problems and symptoms. The diagnosis of heart disease is difficult because there are too many factors to analyze. What's more, the misclassification cost could be very high. Methods: A cost-sensitive ensemble method was proposed to improve the efficiency of diagnosis and reduce the misclassification cost. The proposed method contains five heterogeneous classifiers: random forest, logistic regression, support vector machine, extreme learning machine and k-nearest neighbor. T-test was used to investigate if the performance of the ensemble was better than individual classifiers and the contribution of Relief algorithm. Results: The best performance was achieved by the proposed method according to ten-fold cross validation. The statistical tests demonstrated that the performance of the proposed ensemble was significantly superior to individual classifiers, and the efficiency of classification was distinctively improved by Relief algorithm. Conclusions: The proposed ensemble gained significantly better results compared with individual classifiers and previous studies, which implies that it can be used as a promising alternative tool in medical decision making for heart disease diagnosis.


2020 ◽  
Author(s):  
Zhenya Qi ◽  
Zuoru Zhang

Abstract Background: Heart disease is the primary cause of morbidity and mortality in the world. It includes numerous problems and symptoms. The diagnosis of heart disease is difficult because there are too many factors to analyze. What’s more, the misclassification cost could be very high. Methods: A cost-sensitive ensemble model was proposed to improve the efficiency of diagnosis and reduce the misclassification cost. The proposed model contains five heterogeneous classifiers: random forest, logistic regression, support vector machine, extreme learning machine and k-nearest neighbor. T-test was used to investigate if the performance of the ensemble model was better than individual classifiers and the contribution of Relief algorithm. Results: The best performance was achieved by the proposed model according to ten-fold cross validation. The statistical tests demonstrated that the performance of the proposed model was significantly superior to individual classifiers, and the efficiency of classification was distinctively improved by Relief algorithm. Conclusions: The proposed ensemble model gained significantly better results compared with individual classifiers and previous studies, which implies that it can be used as a promising alternative tool in medical decision making for heart disease diagnosis.


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