Efficient Decision Trees for Multi-class Support Vector Machines Using Entropy and Generalization Error Estimation
This work addresses the problem of efficient multi-class classification for applications requiring fast processing or handling many classes, though it is incremental as it builds on existing SVM and tree-based approaches.
The authors tackled multi-class classification with Support Vector Machines by introducing tree-based methods that use entropy and generalization error estimation to select binary classifiers at each node, achieving time complexity between O(log2N) and O(N) for N classes. Experimental results on UCI datasets with 10-fold cross-validation showed the methods run much faster than traditional techniques while maintaining comparable accuracy.
We propose new methods for Support Vector Machines (SVMs) using tree architecture for multi-class classi- fication. In each node of the tree, we select an appropriate binary classifier using entropy and generalization error estimation, then group the examples into positive and negative classes based on the selected classi- fier and train a new classifier for use in the classification phase. The proposed methods can work in time complexity between O(log2N) to O(N) where N is the number of classes. We compared the performance of our proposed methods to the traditional techniques on the UCI machine learning repository using 10-fold cross-validation. The experimental results show that our proposed methods are very useful for the problems that need fast classification time or problems with a large number of classes as the proposed methods run much faster than the traditional techniques but still provide comparable accuracy.