The Performance of LVQ Based Automatic Relevance Determination Applied to Spontaneous Biosignals
The issue of Automatic Relevance Determination (ARD) has attracted attention over the last decade for the sake of efficiency and accuracy of classifiers, and also to extract knowledge from discriminant functions adapted to a given data set. Based on Learning Vector Quantization (LVQ), we recently proposed an approach to ARD utilizing genetic algorithms. Another approach is the Generalized Relevance LVQ which has been shown to outperform other algorithms of the LVQ family. In the following we present a unique description of a number of LVQ algorithms and compare them concerning their classification accuracy and their efficacy. For this purpose a real world data set consisting of spontaneous EEG and EOG during overnight-driving is employed to detect so-called microsleep events. Results show that relevance learning can improve classification accuracies, but do not reach the performance of Support Vector Machines. The computational costs for the best performing classifiers are exceptionally high and exceed basic LVQ1 by a factor of 104.
KeywordsAutomatic Relevance Determination Learning Vector Quantization Support Vector Machines Electroencephalogram
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