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Hematocrit estimation using online sequential extreme learning machine.
Huynh, Hieu Trung,Won, Yonggwan,Kim, Jinsul Pergamon Press 2015 Bio-medical materials and engineering Vol.26 No.1
<P>Hematocrit is a blood test that is defined as the volume percentage of red blood cells in the whole blood. It is one of the important indicators for clinical decision making and the most effective factor in glucose measurement using handheld devices. In this paper, a method for hematocrit estimation that is based upon the transduced current curve and the neural network is presented. The salient points of this method are that (1) the neural network is trained by the online sequential extreme learning machine (OS-ELM) in which the devices can be still trained with new samples during the using process and (2) the extended features are used to reduce the number of current points which can save the battery power of devices and speed up the measurement process.</P>
Performance Enhancement of SLFNs in Classification by Reducing Effect of Outliers
Hieu Trung Huynh,Yonggwan Won 대한전자공학회 2007 ITC-CSCC :International Technical Conference on Ci Vol.2007 No.7
In this paper, an approach for outlier reduction is proposed to enhance the classification performance of the single-hidden layer feed-forward neural networks (SLFNs). Outliers in the data set are detected based on the distribution on every feature, in which scores are assigned to patterns. Patterns detected as outliers based on these scores will contribute very little in estimating the weights of SLFNs. The experimental results show that, the proposed approach can obtain high accuracy with fast learning speed if there exist outliers in the training set.
Two-Stage Extreme Learning Machine for SLFNs in Regression
Hieu Trung Huynh,Yonggwan Won 대한전자공학회 2007 ITC-CSCC :International Technical Conference on Ci Vol.2007 No.7
Neural network approach has been massively used in regression problem. However, collected data for training often include outliers which affect the final results. In this paper, we propose a new approach for outlier elimination in regression based on the extreme learning machine (ELM) called two-stage ELM. Training process consists of two stages. In the first stage, the single-hidden layer feed forward neural network (SLFN) is trained based on the ELM algorithm using the whole of training set. The trained SLFN is used to verify training patterns. Patterns corresponding to the outputs exceeding a rejection threshold are removed from the training set. Finally, the remainder of training set is used to train the SLFN again based on the ELM algorithm. One interesting observation is that, our approach is simple to detect and eliminate outliers and it can be able to deliver lower error than that of the normal ELM with fast learning speed if there exist outliers in the training set.