Postponing the Evaluation of Attributes with a High Number of Boundary Points
The efficiency of the otherwise expedient decision tree learning can be impaired in processing data-mining-sized data if superlineartime processing is required in attribute selection. An example of such a technique is optimal multisplitting of numerical attributes. Its efficiency is hit hard even by a single troublesome attribute in the domain.