Generation of Attributes for Learning Algorithms

Yuh-Jyh Hu, Dennis Kibler

Inductive algorithms rely strongly on their representational biases, Constructive induction can mitigate representational inadequacies. 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. Unlike most previous research on constructive induction, our methods are designed as preprocessing step before standard machine learning algorithms are applied. We present the results which demonstrate the effectiveness of GALA on artificial and real domains for several learners: C4.5, CN2, perceptron and backpropagation.

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