Tree classifier in spectral space
Ping He, Xiaohua Xu, Ling Chen · 2009
This paper proposes a novel nonlinear decision tree algorithm SSDT, spectral space decision tree. SSDT adopts spectral space transformation to extract the cluster information of data, employs decision tree to discover the decision boundary, and classifies test data with consistent mapping principle. Experimental results show that SSDT can produce higher classification accuracy and better generalization ability than the traditional decision tree algorithms.