A Template for Automatic Generation of Decision Trees
Xiaoping Liu · Jisuanji fangzhen · 2005
Most data mining tools for knowledge discovery generally use rule discovery and decision tree technology to extract data patterns and rules. A general template for automatic generation of decision trees is provided by attribute based descrization methor, by statistic based unknown attributes and noisy data processing method, and by error based pruning method. The designers of automatic tree building algorithms can quickly evaluate the new algorithms for solving specific decision problems with this template. The basic mechanism for creating new tree building is a generic algorithm template, which is initialized by the algorithm designers with his own formula. Using the interactive graphic environment provided by this system, the new algorithms can be tested on different decision problems.