Feature selection consideration for multi-class cardiac arrhythmia classification

This paper presents the performance of support vector machine to classify the multi-class arrhythmia dataset by pre-selecting sets of feature that best suit the training data set in two-class fashion. By allowing freedom of feature dimension selection in different grouping in classification procedure, the classification performance is comparable to one that uses constant feature dimension but with less computational complexity.