I am working on ECG arrhythmia classification by using SVM , implemented some kernels tricks
and using different kernels on MIT BIH dataset (features create 44187 row ,18 column matrix)
now it is difficult to plot support vector for such large data sets , now how can i plot it and please suggest any other plots or methods to show comparison between different kernels , i already have comparison chart of accuracy efficiency etc.
It might interest you that there is a possibility to use complexity measures to assess the state of the observed complex system and make decision about arrhythmias.
An example how to do it can be bound in our paper on prediction of TdP arrhythmias from ECG recordings. Everything is explained in the paper in detail. The final version will contain rewritten entropy section and substantially improved methods, intro, etc.
Back to your question. Complexity measures when applied wisely enable us to substantially reduce the complexity of complex systems under the observation. This includes biosignals along with ECGs, EEGs, etc.
Hopefully this will enable you to orientate yourself in this exciting, yet quite complicated area of research.
Try ggplot function in ggplot2 package and use fill up to compare two different algo for one performance measures.
1) https://core.ac.uk/download/pdf/188109752.pdf
This may help you
Regards
https://scikit-learn.org/0.18/auto_examples/svm/plot_iris.html
This might help you
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