Multistrategy Constructive Induction: AQ17-MCI
Eric Bloedorn, Janusz Wnek, Ryszard S. Michalski · 1993
This paper presents a method for multistrategy constructive induction that integrates two inferential learning strategies—empirical induction and deduction, and two computational methods—data-driven and hypothesis-driven. The method generates inductive hypotheses in an iteratively modified representation space. The operators modifying the representation space are classified into "constructors, " which expand the space (by generating additional attributes) and "destructors " which contract the space (by removing low relevance attributes or abstracting attribute values). Constructors generate new dimensions (attributes) by analyzing original or transformed examples (data-driven) and by analyzing the rules obtained in the previous iteration (hypothesisdriven). Destructors detect the irrelevant components of the representation space by rulebased inference or statistical analysis. The method has been implemented in the AQ17-MCI program. The preliminary results from applying it to a problem with noisy training data and large number of irrelevant attributes demonstrated a superiority of the method over other constructive induction methods both in terms of the predictive accuracy, as well as the overall simplicity of the generated descriptions. Key words: multistrategy learning, inductive inference, constructive induction, representation space, concept learning. 1.