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This paper proposes a fast and accurate feature extraction and classification algorithm to detect excess water in lungs using lung sounds. The proposed design uses a three-part Segmented Sub-band Feature Extractor to extract features. The first part extracts features by segmenting the frequencies found in these sounds into bins through sub-bands. The second part uses Principle Component Analysis and...
In this paper, a voting based weighted online sequential extreme learning machine (VWOS-ELM) is proposed for class imbalance learning (CIL). VWOS-ELM is the first sequential classifier that can tackle the class imbalance problem in multi-class data streams. Utilizing WOS-ELM and the recently proposed voting based online sequential extreme learning machine (VOS-ELM) method, VWOS-ELM adapts better to...
The performance of support vector machines (SVMs) can deteriorate when the number of samples in one class is much greater than that in the other. Existing methods tackle this problem by modifying the learning algorithms or resampling the datasets. In this paper, we propose a new method called one-vs-all for class imbalance learning (OVACIL) which neither modifies the SVM learning algorithms nor resamples...
In this paper, we propose a voting based online sequential extreme learning machine (VOS-ELM) for single hidden layer feedforward networks (SLFNs) to perform the online sequential multi-class classification. Utilizing the recent voting based extreme learning machine (V-ELM) and the online sequential extreme learning machine (OS-ELM), the newly developed VOS-ELM is able to classify online sequences...
Heart rate variability (HRV) is a non-invasive measurement that has shown promise as an indicator of cardiovascular, respiratory and metabolic dynamics. In this study, three different classification techniques, i.e. extreme learning machine (ELM), support vector machine (SVM) and back-propagation based neural network (BP), were investigated to classify HRV signals obtained from electrocardiograms...
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