A Soft Discretization Technique for Fuzzy Decision Trees Using Resampling

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Abstract

Decision trees generate classifiers from training data through a process of recursively splitting the data space. In the case of training on continuous-valued data, the associated attributes must be discretized into several intervals using a set of crisp cut points. One drawback of decision trees is their instability, i.e., small data deviations may require a significant reconstruction of the decision tree. Here, we present a novel soft decision tree method that uses soft of fuzzy discretization instead of traditional crisp cuts. We use a resampling based technique to generate soft discretization points and demonstrate the advantages of using our resampling based soft discretization over traditional crisp methods.