Abstract
Inductive algorithms rely strongly on their representational biases. Representational inadequacy can be mitigated by constructive induction. This chapter introduces several important issues for constructive induction and describes a new multi-strategy constructive induction algorithm (GALA2.0) which is independent of the learning algorithm. Unlike most previous research on constructive induction, our methods are designed as a preprocessing step before standard machine learning algorithms are applied. We present the results which demonstrate the effectiveness of GALA2.0 on real domains for two different learners: C4.5 and backpropagation.
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Hu, YJ. (1998). Constructive Induction: Covering Attribute Spectrum. In: Liu, H., Motoda, H. (eds) Feature Extraction, Construction and Selection. The Springer International Series in Engineering and Computer Science, vol 453. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-5725-8_16
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DOI: https://doi.org/10.1007/978-1-4615-5725-8_16
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