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In heterogeneous ensemble systems, each learning algorithm learns a classifier on a given training set to describe the relationship between a feature vector and its class label. As each classifier outputs different result on an observation, uncertainty is introduced. In this paper, we introduce a heterogeneous ensemble system with a fuzzy IF-THEN rule inference engine as the combiner to capture the...
This paper proposes an adaptive denoising methodology for electrocardiogram (ECG) signals that employs ensemble empirical mode decomposition (EEMD) and a genetic algorithm (GA)-based thresholding technique. In this method, a noisy ECG signal is first decomposed by means of EEMD into a series of intrinsic mode functions (IMFs), which are then separated into signal- and noise-dominant groups using a...
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