C4.5: programs for machine learning
C4.5: programs for machine learning
Fuzzy Sets and Systems - Special issue on fuzzy optimization
On the handling of fuzziness for continuous-valued attributes in decision tree generation
Fuzzy Sets and Systems
Application of Fuzzy Rule Induction to Data Mining
FQAS '98 Proceedings of the Third International Conference on Flexible Query Answering Systems
A complete fuzzy decision tree technique
Fuzzy Sets and Systems - Theme: Learning and modeling
A Mathematical Theory of Communication
A Mathematical Theory of Communication
A system for induction of oblique decision trees
Journal of Artificial Intelligence Research
Fuzzy decision trees: issues and methods
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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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.