Abstract
An inductive learning program's ability to find an accurate hypothesis can depend on the quality of the representation space. The authors have developed a data-driven constructive-induction method that uses multiple operators to improve the representation space. They have applied it to two real-world problems. Constructive-induction integrates ideas and methods previously considered separate: attribute selection, construction, and abstraction. By integrating these methods into AQ17-DCI, they were able to increase predictive accuracy by up to 29% in their test cases.
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