Title :
Data-driven constructive induction
Author :
Bloedorn, Eric ; Michalsi, R.S.
Author_Institution :
George Mason Univ., Fairfax, VA, USA
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
Keywords :
data analysis; knowledge acquisition; knowledge representation; learning by example; AQ17-DCI; abstraction; accurate hypothesis; attribute selection; construction; data mining; data-driven constructive induction; inductive learning program; multiple operators; representation space; Accuracy; Human computer interaction; Intelligent systems;
Journal_Title :
Intelligent Systems and their Applications, IEEE
DOI :
10.1109/5254.671089
