Inducing Multivariate Decision Trees with the R>sup/sup<-rule
Takaharu Kawatsure, Qiangfu Zhao · 2006
Decision tree (DT) is often considered as a comprehensible learning model. If the data set is large, however, the induced DT may be too large to understand. Currently, we have proposed a non-genetic evolutionary algorithm called R/sup 4/-rule for producing the smallest nearest neighbor classifiers (NNCs). In this paper, we propose two new approaches for inducing DTs with the R/sup 4/-rule. The DTs considered here are multivariate, and there is an NNC with two or more prototypes in each non-terminal node. In the first method, the prototypes are found directly from the training set. In the second method, the prototypes are found from the data assigned to each nonterminal node. Using these methods, we can induce more compact and more comprehensible DTs. The efficiency and efficacy of the methods are verified through experiments with several public databases.