Comparison of Bagging and Boosting Ensemble Machine Learning Methods for Automated EMG Signal Classification

Abstract

The neuromuscular disorders are diagnosed using electromyographic (EMG) signals. Machine learning algorithms are employed as a decision support system to diagnose neuromuscular disorders. This paper compares bagging and boosting ensemble learning methods to classify EMG signals automatically. Even though ensemble classifiers' efficacy in relation to real-life issues has been presented in numerous studies, there are almost no studies which focus on the feasibility of bagging and boosting ensemble classifiers to diagnose the neuromuscular disorders. Therefore, the purpose of this paper is to assess the feasibility of bagging and boosting ensemble classifiers to diagnose neuromuscular disorders through the use of EMG signals. It should be understood that there are three steps to this method, where the step number one is to calculate the wavelet packed coefficients (WPC) for every type of EMG signal. After this, it is necessary to calculate statistical values of WPC so that the distribution of wavelet coefficients could be demonstrated. In the last step, an ensemble classifier used the extracted features as an input of the classifier to diagnose the neuromuscular disorders. Experimental results showed the ensemble classifiers achieved better performance for diagnosis of neuromuscular disorders. Results are promising and showed that the AdaBoost with random forest ensemble method achieved an accuracy of 99.08%, F-measure 0.99, AUC 1, and kappa statistic 0.99.

Development of machine learning model for diagnostic disease prediction based on laboratory tests

“…Ensemble modeling is a method of creating strong learners by combining weak learners, and has been widely used for ML recently. Boosting and bagging 30 – 32 are the most common ensemble methods. Bagging creates a generalized model through bootstrapping (random sampling) of datasets followed by aggregation into different datasets.…”

Ensemble Algorithms and Their Applications

“…The most popular and widely used ensemble methods are the error-based and the simple average [82]. The easiest among them is the simple average in which all forecasts are weighted equally, often remarkably improving overall forecasting accuracy [82,83].…”

“…One of the bagging ensemble algorithms investigated in this study was random decision forest (RDF), which is an extension over bagging that is popularly called random forest [75,82]. RDF can be constituted by making use of bagging based on the CART approach to raise trees [83]. The other bagging ensemble learning algorithms investigated in this study were Bagging with SVM as the base learner (BSVM) and bagging with the multilayer perceptron neural network as the base learner (BMLP).…”

“…For instance, researchers may give a higher weight to a model with lower error, while they may assign a less weight value to a model that presents more error, respectively. In most of the cases, the forecasting error is calculated using total absolute error statistic, such as the sum of squared error (SSE) [80,83]. The combining weight for individual prediction is mathematically given by:…”

Ensemble Learning of Hybrid Acoustic Features for Speech Emotion Recognition

“…One of the bagging ensemble algorithms investigated in this study was random decision forest (RDF), which is an extension over bagging that is popularly called random forest [75,82]. RDF can be constituted by making use of bagging based on the CART approach to raise trees [83]. The other bagging ensemble learning algorithms investigated in this study were Bagging with SVM as the base learner (BSVM) and bagging with the multilayer perceptron neural network as the base learner (BMLP).…”