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A Wrapper Approach for Constructive Induction

Inductive algorithms rely strongly on their representational biases. Representational inadequacy can be mitigated by constructive induction. This paper introduces the notion of a relative gain measure and describes a new constructive induction algorithm (GALA) which is independent of the learning algorithm. GALA generates a small number of new boolean attributes from existing boolean, nominal or real-valued attributes. Unlike most previous research on constructive induction, our methods are designed as preprocessing step before standard machine learning algorithms are applied. We present results which demonstrate the e ectiveness of GALA on both arti cial and real domains for both symbolic and subsymbolic learners. For symbolic learners, we used C4.5 and CN2. For subsymbolic learners, we used perceptron and backpropagation. In all cases, the GALA preprocessor increased the performance of the learning algorithm.