Efficient Decision Trees for Multi-class Support Vector Machines Using Large Centroid Distance Grouping
Pittipol Kantavat, Patoomsiri Songsiri, Boonserm Kijsirikul · Engineering Journal · 2022
We propose a new technique for support vector machines (SVMs) in tree structures for multiclass classification.For each tree node, we select an appropriate binary classifier using data class centroids and their in-between distances, categorize the training examples into positive and negative groups of classes and train a new classifier.The proposed technique is fast-trained and can classify an output class data with a complexity between O(log 2 N ) and O(N ) where N is the number of classes.The 10-fold cross-validation experimental results show that the performance of our methods is comparable to that of traditional techniques and required less decision times.Our proposed technique is suitable for problems with a large number of classes due to its advantages of requiring less training time and computational complexity.